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<h1 class="mume-header" id="%E5%9B%BE%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C%E5%AF%BC%E8%AE%BA">图神经网络导论</h1>

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<h2 class="mume-header" id="%E7%A5%9E%E7%BB%8F%E7%BD%91%E7%BB%9C">神经网络</h2>

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<h4 class="mume-header" id="tengzhanghusteducn"><a href="mailto:tengzhang@hust.edu.cn">tengzhang@hust.edu.cn</a></h4>

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<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>发展史</h5></div></div>
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todayMarker off
dateFormat  YYYY
axisFormat %Y

section 神经网络
模型提出: done, 1943, 1969
1943 MP神经网络: 1943, milestone
1958 Rosenblatt提出感知机: 1958, milestone
1969 Minsky出版《感知机》: 1969, milestone
冰河期: done, 1969, 1983
1974 反向传播被提出: 1974, milestone
1980 带卷积和子采样的新知机: 1980, milestone
复兴: done, 1983, 1995
1983 Hopfield网络: 1983, milestone
1984 Boltzmann机: 1984, milestone
1986 反向传播被重新提出: 1986, milestone
二次冰河: done, 1995, 2006
1995 统计机器学习兴起: 1995, milestone
深度学习: active, 2006, 2021
2012 DNN引起轰动: 2012, milestone
</div><div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="38" class="slide " data-line="38" data-h="1" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>神经元</h5></div></div>
<p>神经网络的基本结构称为神经元</p>
<br>
<p>单个神经元对应运算<span class="mathjax-exps">$y = h(\wv^\top \xv + b)$</span>，其中<span class="mathjax-exps">$h$</span>是非线性<span class="blue">激活函数</span></p>
<div class="sparse top10 left10">
<h3>下图激活函数<span class="mathjax-exps">$h = \sgn(\cdot)$</span></h3>
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" class="width45 left20 top-12">
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="58" class="slide " data-line="58" data-h="1" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>神经网络</h5></div></div>
<p>将神经元广泛并行互连就构成了神经网络</p>
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<polygon fill="#586e75" stroke="#586e75" points="106.8416,-92.7086 101.3726,-93.0997 104.4944,-91.8479 102.1472,-90.9872 102.1472,-90.9872 102.1472,-90.9872 104.4944,-91.8479 102.9218,-88.8748 106.8416,-92.7086 106.8416,-92.7086"></polygon>
</g>
<!-- 12&#45;&gt;24 -->
<g id="edge9" class="edge">
<title>12-&gt;24</title>
<path fill="none" stroke="#586e75" d="M51.3529,-59.8301C65.763,-54.7065 86.3487,-47.3871 101.9296,-41.8472"></path>
<polygon fill="#586e75" stroke="#586e75" points="107.0056,-40.0425 103.0483,-43.8376 104.65,-40.88 102.2945,-41.7176 102.2945,-41.7176 102.2945,-41.7176 104.65,-40.88 101.5407,-39.5976 107.0056,-40.0425 107.0056,-40.0425"></polygon>
</g>
<!-- 13 -->
<g id="node3" class="node">
<title>13</title>
<ellipse fill="none" stroke="#859900" cx="34" cy="-196" rx="18" ry="18"></ellipse>
</g>
<!-- 13&#45;&gt;21 -->
<g id="edge3" class="edge">
<title>13-&gt;21</title>
<path fill="none" stroke="#859900" d="M50.9283,-202.207C65.4181,-207.52 86.3732,-215.2035 102.131,-220.9814"></path>
<polygon fill="#859900" stroke="#859900" points="106.8416,-222.7086 101.3726,-223.0997 104.4944,-221.8479 102.1472,-220.9872 102.1472,-220.9872 102.1472,-220.9872 104.4944,-221.8479 102.9218,-218.8748 106.8416,-222.7086 106.8416,-222.7086"></polygon>
</g>
<!-- 13&#45;&gt;22 -->
<g id="edge10" class="edge">
<title>13-&gt;22</title>
<path fill="none" stroke="#586e75" d="M51.3529,-189.8301C65.763,-184.7065 86.3487,-177.3871 101.9296,-171.8472"></path>
<polygon fill="#586e75" stroke="#586e75" points="107.0056,-170.0425 103.0483,-173.8376 104.65,-170.88 102.2945,-171.7176 102.2945,-171.7176 102.2945,-171.7176 104.65,-170.88 101.5407,-169.5976 107.0056,-170.0425 107.0056,-170.0425"></polygon>
</g>
<!-- 13&#45;&gt;23 -->
<g id="edge11" class="edge">
<title>13-&gt;23</title>
<path fill="none" stroke="#586e75" d="M46.576,-182.6318C51.9532,-176.9039 58.2945,-170.1332 64,-164 78.9777,-147.8995 95.9713,-129.4749 108.04,-116.363"></path>
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</g>
<!-- 13&#45;&gt;24 -->
<g id="edge12" class="edge">
<title>13-&gt;24</title>
<path fill="none" stroke="#586e75" d="M47.6109,-183.9447C53.2954,-178.3373 59.589,-171.2998 64,-164 87.8431,-124.5419 75.5691,-106.2776 98,-66 100.8265,-60.9247 104.4739,-55.8513 108.1376,-51.2887"></path>
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</g>
<!-- 31 -->
<g id="node8" class="node">
<title>31</title>
<ellipse fill="none" stroke="#586e75" cx="210" cy="-131" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;31 -->
<g id="edge13" class="edge">
<title>21-&gt;31</title>
<path fill="none" stroke="#586e75" d="M135.9579,-215.3735C151.1474,-198.0646 177.4023,-168.1462 194.2211,-148.9806"></path>
<polygon fill="#586e75" stroke="#586e75" points="197.8664,-144.8267 196.2596,-150.0689 196.2174,-146.7058 194.5684,-148.5848 194.5684,-148.5848 194.5684,-148.5848 196.2174,-146.7058 192.8772,-147.1007 197.8664,-144.8267 197.8664,-144.8267"></polygon>
</g>
<!-- 32 -->
<g id="node9" class="node">
<title>32</title>
<ellipse fill="none" stroke="#586e75" cx="210" cy="-196" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;32 -->
<g id="edge14" class="edge">
<title>21-&gt;32</title>
<path fill="none" stroke="#586e75" d="M140.9908,-222.4803C154.5366,-217.2825 173.5981,-209.9682 188.2565,-204.3434"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-202.5062 189.1824,-206.3982 190.7104,-203.4019 188.3763,-204.2975 188.3763,-204.2975 188.3763,-204.2975 190.7104,-203.4019 187.5702,-202.1969 193.0445,-202.5062 193.0445,-202.5062"></polygon>
</g>
<!-- 33 -->
<g id="node10" class="node">
<title>33</title>
<ellipse fill="none" stroke="#586e75" cx="210" cy="-66" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;33 -->
<g id="edge15" class="edge">
<title>21-&gt;33</title>
<path fill="none" stroke="#586e75" d="M133.1007,-213.119C135.9903,-207.998 139.1691,-202.2808 142,-197 165.102,-153.9046 169.1795,-142.2451 192,-99 194.1472,-94.931 196.4769,-90.6003 198.7258,-86.4608"></path>
<polygon fill="#586e75" stroke="#586e75" points="201.1218,-82.0654 200.7041,-87.5325 199.9252,-84.2605 198.7286,-86.4555 198.7286,-86.4555 198.7286,-86.4555 199.9252,-84.2605 196.753,-85.3786 201.1218,-82.0654 201.1218,-82.0654"></polygon>
</g>
<!-- 22&#45;&gt;31 -->
<g id="edge16" class="edge">
<title>22-&gt;31</title>
<path fill="none" stroke="#586e75" d="M140.9908,-157.4803C154.5366,-152.2825 173.5981,-144.9682 188.2565,-139.3434"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-137.5062 189.1824,-141.3982 190.7104,-138.4019 188.3763,-139.2975 188.3763,-139.2975 188.3763,-139.2975 190.7104,-138.4019 187.5702,-137.1969 193.0445,-137.5062 193.0445,-137.5062"></polygon>
</g>
<!-- 22&#45;&gt;32 -->
<g id="edge17" class="edge">
<title>22-&gt;32</title>
<path fill="none" stroke="#586e75" d="M140.9908,-170.3222C154.5366,-175.3624 173.5981,-182.4551 188.2565,-187.9094"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-189.691 187.5737,-190.056 190.7014,-188.8191 188.3583,-187.9472 188.3583,-187.9472 188.3583,-187.9472 190.7014,-188.8191 189.143,-185.8385 193.0445,-189.691 193.0445,-189.691"></polygon>
</g>
<!-- 22&#45;&gt;33 -->
<g id="edge18" class="edge">
<title>22-&gt;33</title>
<path fill="none" stroke="#586e75" d="M135.9579,-150.3735C151.1474,-133.0646 177.4023,-103.1462 194.2211,-83.9806"></path>
<polygon fill="#586e75" stroke="#586e75" points="197.8664,-79.8267 196.2596,-85.0689 196.2174,-81.7058 194.5684,-83.5848 194.5684,-83.5848 194.5684,-83.5848 196.2174,-81.7058 192.8772,-82.1007 197.8664,-79.8267 197.8664,-79.8267"></polygon>
</g>
<!-- 23&#45;&gt;31 -->
<g id="edge19" class="edge">
<title>23-&gt;31</title>
<path fill="none" stroke="#586e75" d="M140.9908,-105.3222C154.5366,-110.3624 173.5981,-117.4551 188.2565,-122.9094"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-124.691 187.5737,-125.056 190.7014,-123.8191 188.3583,-122.9472 188.3583,-122.9472 188.3583,-122.9472 190.7014,-123.8191 189.143,-120.8385 193.0445,-124.691 193.0445,-124.691"></polygon>
</g>
<!-- 23&#45;&gt;32 -->
<g id="edge20" class="edge">
<title>23-&gt;32</title>
<path fill="none" stroke="#586e75" d="M135.9579,-112.4874C151.1474,-129.6197 177.4023,-159.2329 194.2211,-178.2029"></path>
<polygon fill="#586e75" stroke="#586e75" points="197.8664,-182.3144 192.8657,-180.0658 196.2079,-180.4437 194.5493,-178.5731 194.5493,-178.5731 194.5493,-178.5731 196.2079,-180.4437 196.2329,-177.0804 197.8664,-182.3144 197.8664,-182.3144"></polygon>
</g>
<!-- 23&#45;&gt;33 -->
<g id="edge21" class="edge">
<title>23-&gt;33</title>
<path fill="none" stroke="#586e75" d="M140.9908,-92.4803C154.5366,-87.2825 173.5981,-79.9682 188.2565,-74.3434"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-72.5062 189.1824,-76.3982 190.7104,-73.4019 188.3763,-74.2975 188.3763,-74.2975 188.3763,-74.2975 190.7104,-73.4019 187.5702,-72.1969 193.0445,-72.5062 193.0445,-72.5062"></polygon>
</g>
<!-- 24&#45;&gt;31 -->
<g id="edge22" class="edge">
<title>24-&gt;31</title>
<path fill="none" stroke="#586e75" d="M135.9579,-47.4874C151.1474,-64.6197 177.4023,-94.2329 194.2211,-113.2029"></path>
<polygon fill="#586e75" stroke="#586e75" points="197.8664,-117.3144 192.8657,-115.0658 196.2079,-115.4437 194.5493,-113.5731 194.5493,-113.5731 194.5493,-113.5731 196.2079,-115.4437 196.2329,-112.0804 197.8664,-117.3144 197.8664,-117.3144"></polygon>
</g>
<!-- 24&#45;&gt;32 -->
<g id="edge23" class="edge">
<title>24-&gt;32</title>
<path fill="none" stroke="#586e75" d="M133.1007,-49.881C135.9903,-55.002 139.1691,-60.7192 142,-66 165.102,-109.0954 168.898,-120.9046 192,-164 194.0347,-167.7956 196.2491,-171.8166 198.4067,-175.6796"></path>
<polygon fill="#586e75" stroke="#586e75" points="200.8993,-180.119 196.4894,-176.8608 199.6753,-177.9391 198.4514,-175.7592 198.4514,-175.7592 198.4514,-175.7592 199.6753,-177.9391 200.4133,-174.6577 200.8993,-180.119 200.8993,-180.119"></polygon>
</g>
<!-- 24&#45;&gt;33 -->
<g id="edge24" class="edge">
<title>24-&gt;33</title>
<path fill="none" stroke="#586e75" d="M140.9908,-40.3222C154.5366,-45.3624 173.5981,-52.4551 188.2565,-57.9094"></path>
<polygon fill="#586e75" stroke="#586e75" points="193.0445,-59.691 187.5737,-60.056 190.7014,-58.8191 188.3583,-57.9472 188.3583,-57.9472 188.3583,-57.9472 190.7014,-58.8191 189.143,-55.8385 193.0445,-59.691 193.0445,-59.691"></polygon>
</g>
<!-- 41 -->
<g id="node11" class="node">
<title>41</title>
<ellipse fill="none" stroke="#586e75" cx="296" cy="-34" rx="18" ry="18"></ellipse>
</g>
<!-- 31&#45;&gt;41 -->
<g id="edge25" class="edge">
<title>31-&gt;41</title>
<path fill="none" stroke="#586e75" d="M221.9579,-117.5126C237.1474,-100.3803 263.4023,-70.7671 280.2211,-51.7971"></path>
<polygon fill="#586e75" stroke="#586e75" points="283.8664,-47.6856 282.2329,-52.9196 282.2079,-49.5563 280.5493,-51.4269 280.5493,-51.4269 280.5493,-51.4269 282.2079,-49.5563 278.8657,-49.9342 283.8664,-47.6856 283.8664,-47.6856"></polygon>
</g>
<!-- 42 -->
<g id="node12" class="node">
<title>42</title>
<ellipse fill="none" stroke="#586e75" cx="296" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 31&#45;&gt;42 -->
<g id="edge26" class="edge">
<title>31-&gt;42</title>
<path fill="none" stroke="#586e75" d="M226.9908,-124.6778C240.5366,-119.6376 259.5981,-112.5449 274.2565,-107.0906"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-105.309 275.143,-109.1615 276.7014,-106.1809 274.3583,-107.0528 274.3583,-107.0528 274.3583,-107.0528 276.7014,-106.1809 273.5737,-104.944 279.0445,-105.309 279.0445,-105.309"></polygon>
</g>
<!-- 43 -->
<g id="node13" class="node">
<title>43</title>
<ellipse fill="none" stroke="#586e75" cx="296" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 31&#45;&gt;43 -->
<g id="edge27" class="edge">
<title>31-&gt;43</title>
<path fill="none" stroke="#586e75" d="M226.9908,-137.5197C240.5366,-142.7175 259.5981,-150.0318 274.2565,-155.6566"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-157.4938 273.5702,-157.8031 276.7104,-156.5981 274.3763,-155.7025 274.3763,-155.7025 274.3763,-155.7025 276.7104,-156.5981 275.1824,-153.6018 279.0445,-157.4938 279.0445,-157.4938"></polygon>
</g>
<!-- 44 -->
<g id="node14" class="node">
<title>44</title>
<ellipse fill="none" stroke="#586e75" cx="296" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 31&#45;&gt;44 -->
<g id="edge28" class="edge">
<title>31-&gt;44</title>
<path fill="none" stroke="#586e75" d="M221.9579,-144.6265C237.1474,-161.9354 263.4023,-191.8538 280.2211,-211.0194"></path>
<polygon fill="#586e75" stroke="#586e75" points="283.8664,-215.1733 278.8772,-212.8993 282.2174,-213.2942 280.5684,-211.4152 280.5684,-211.4152 280.5684,-211.4152 282.2174,-213.2942 282.2596,-209.9311 283.8664,-215.1733 283.8664,-215.1733"></polygon>
</g>
<!-- 32&#45;&gt;41 -->
<g id="edge29" class="edge">
<title>32-&gt;41</title>
<path fill="none" stroke="#586e75" d="M219.1007,-180.119C221.9903,-174.998 225.1691,-169.2808 228,-164 251.102,-120.9046 254.898,-109.0954 278,-66 280.0347,-62.2044 282.2491,-58.1834 284.4067,-54.3204"></path>
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</g>
<!-- 32&#45;&gt;42 -->
<g id="edge30" class="edge">
<title>32-&gt;42</title>
<path fill="none" stroke="#586e75" d="M221.9579,-182.5126C237.1474,-165.3803 263.4023,-135.7671 280.2211,-116.7971"></path>
<polygon fill="#586e75" stroke="#586e75" points="283.8664,-112.6856 282.2329,-117.9196 282.2079,-114.5563 280.5493,-116.4269 280.5493,-116.4269 280.5493,-116.4269 282.2079,-114.5563 278.8657,-114.9342 283.8664,-112.6856 283.8664,-112.6856"></polygon>
</g>
<!-- 32&#45;&gt;43 -->
<g id="edge31" class="edge">
<title>32-&gt;43</title>
<path fill="none" stroke="#586e75" d="M226.9908,-189.6778C240.5366,-184.6376 259.5981,-177.5449 274.2565,-172.0906"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-170.309 275.143,-174.1615 276.7014,-171.1809 274.3583,-172.0528 274.3583,-172.0528 274.3583,-172.0528 276.7014,-171.1809 273.5737,-169.944 279.0445,-170.309 279.0445,-170.309"></polygon>
</g>
<!-- 32&#45;&gt;44 -->
<g id="edge32" class="edge">
<title>32-&gt;44</title>
<path fill="none" stroke="#586e75" d="M226.9908,-202.5197C240.5366,-207.7175 259.5981,-215.0318 274.2565,-220.6566"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-222.4938 273.5702,-222.8031 276.7104,-221.5981 274.3763,-220.7025 274.3763,-220.7025 274.3763,-220.7025 276.7104,-221.5981 275.1824,-218.6018 279.0445,-222.4938 279.0445,-222.4938"></polygon>
</g>
<!-- 33&#45;&gt;41 -->
<g id="edge33" class="edge">
<title>33-&gt;41</title>
<path fill="none" stroke="#586e75" d="M226.9908,-59.6778C240.5366,-54.6376 259.5981,-47.5449 274.2565,-42.0906"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-40.309 275.143,-44.1615 276.7014,-41.1809 274.3583,-42.0528 274.3583,-42.0528 274.3583,-42.0528 276.7014,-41.1809 273.5737,-39.944 279.0445,-40.309 279.0445,-40.309"></polygon>
</g>
<!-- 33&#45;&gt;42 -->
<g id="edge34" class="edge">
<title>33-&gt;42</title>
<path fill="none" stroke="#586e75" d="M226.9908,-72.5197C240.5366,-77.7175 259.5981,-85.0318 274.2565,-90.6566"></path>
<polygon fill="#586e75" stroke="#586e75" points="279.0445,-92.4938 273.5702,-92.8031 276.7104,-91.5981 274.3763,-90.7025 274.3763,-90.7025 274.3763,-90.7025 276.7104,-91.5981 275.1824,-88.6018 279.0445,-92.4938 279.0445,-92.4938"></polygon>
</g>
<!-- 33&#45;&gt;43 -->
<g id="edge35" class="edge">
<title>33-&gt;43</title>
<path fill="none" stroke="#586e75" d="M221.9579,-79.6265C237.1474,-96.9354 263.4023,-126.8538 280.2211,-146.0194"></path>
<polygon fill="#586e75" stroke="#586e75" points="283.8664,-150.1733 278.8772,-147.8993 282.2174,-148.2942 280.5684,-146.4152 280.5684,-146.4152 280.5684,-146.4152 282.2174,-148.2942 282.2596,-144.9311 283.8664,-150.1733 283.8664,-150.1733"></polygon>
</g>
<!-- 33&#45;&gt;44 -->
<g id="edge36" class="edge">
<title>33-&gt;44</title>
<path fill="none" stroke="#586e75" d="M218.8782,-82.0654C221.8139,-87.4333 225.0761,-93.4592 228,-99 250.8205,-142.2451 254.898,-153.9046 278,-197 280.0347,-200.7956 282.2491,-204.8166 284.4067,-208.6796"></path>
<polygon fill="#586e75" stroke="#586e75" points="286.8993,-213.119 282.4894,-209.8608 285.6753,-210.9391 284.4514,-208.7592 284.4514,-208.7592 284.4514,-208.7592 285.6753,-210.9391 286.4133,-207.6577 286.8993,-213.119 286.8993,-213.119"></polygon>
</g>
<!-- 51 -->
<g id="node15" class="node">
<title>51</title>
<ellipse fill="none" stroke="#586e75" cx="382" cy="-131" rx="18" ry="18"></ellipse>
</g>
<!-- 41&#45;&gt;51 -->
<g id="edge37" class="edge">
<title>41-&gt;51</title>
<path fill="none" stroke="#586e75" d="M307.9579,-47.4874C323.1474,-64.6197 349.4023,-94.2329 366.2211,-113.2029"></path>
<polygon fill="#586e75" stroke="#586e75" points="369.8664,-117.3144 364.8657,-115.0658 368.2079,-115.4437 366.5493,-113.5731 366.5493,-113.5731 366.5493,-113.5731 368.2079,-115.4437 368.2329,-112.0804 369.8664,-117.3144 369.8664,-117.3144"></polygon>
</g>
<!-- 52 -->
<g id="node16" class="node">
<title>52</title>
<ellipse fill="none" stroke="#586e75" cx="382" cy="-196" rx="18" ry="18"></ellipse>
</g>
<!-- 41&#45;&gt;52 -->
<g id="edge38" class="edge">
<title>41-&gt;52</title>
<path fill="none" stroke="#586e75" d="M305.1007,-49.881C307.9903,-55.002 311.1691,-60.7192 314,-66 337.102,-109.0954 340.898,-120.9046 364,-164 366.0347,-167.7956 368.2491,-171.8166 370.4067,-175.6796"></path>
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</g>
<!-- 53 -->
<g id="node17" class="node">
<title>53</title>
<ellipse fill="none" stroke="#586e75" cx="382" cy="-66" rx="18" ry="18"></ellipse>
</g>
<!-- 41&#45;&gt;53 -->
<g id="edge39" class="edge">
<title>41-&gt;53</title>
<path fill="none" stroke="#586e75" d="M312.9908,-40.3222C326.5366,-45.3624 345.5981,-52.4551 360.2565,-57.9094"></path>
<polygon fill="#586e75" stroke="#586e75" points="365.0445,-59.691 359.5737,-60.056 362.7014,-58.8191 360.3583,-57.9472 360.3583,-57.9472 360.3583,-57.9472 362.7014,-58.8191 361.143,-55.8385 365.0445,-59.691 365.0445,-59.691"></polygon>
</g>
<!-- 42&#45;&gt;51 -->
<g id="edge40" class="edge">
<title>42-&gt;51</title>
<path fill="none" stroke="#586e75" d="M312.9908,-105.3222C326.5366,-110.3624 345.5981,-117.4551 360.2565,-122.9094"></path>
<polygon fill="#586e75" stroke="#586e75" points="365.0445,-124.691 359.5737,-125.056 362.7014,-123.8191 360.3583,-122.9472 360.3583,-122.9472 360.3583,-122.9472 362.7014,-123.8191 361.143,-120.8385 365.0445,-124.691 365.0445,-124.691"></polygon>
</g>
<!-- 42&#45;&gt;52 -->
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</p><div></div>
<p>只要存在隐藏层，模型就拥有了非线性预测能力</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="72" class="slide " data-line="72" data-h="1" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>形式化</h5></div></div>
<p><span class="mathjax-exps">$L$</span>：神经网络的层数</p>
<p><span class="mathjax-exps">$n_l$</span>：第<span class="mathjax-exps">$l$</span>层神经元的个数</p>
<p><span class="mathjax-exps">$h_l(\cdot)$</span>：第<span class="mathjax-exps">$l$</span>层的激活函数</p>
<p><span class="mathjax-exps">$\Wv_l \in \Rbb^{n_l \times n_{l-1}}$</span>：第<span class="mathjax-exps">$l-1$</span>层到第<span class="mathjax-exps">$l$</span>层的权重矩阵</p>
<p><span class="mathjax-exps">$\bv_l \in \Rbb^{n_l}$</span>：第<span class="mathjax-exps">$l$</span>层的偏置 (截距)</p>
<p><span class="mathjax-exps">$\zv_l \in \Rbb^{n_l}$</span>：第<span class="mathjax-exps">$l$</span>层神经元的输入</p>
<p><span class="mathjax-exps">$\av_l \in \Rbb^{n_l}$</span>：第<span class="mathjax-exps">$l$</span>层神经元的输出</p>
<br>
<p>第<span class="mathjax-exps">$l$</span>层的计算过程：<span class="mathjax-exps">$\zv_l = \Wv_l \av_{l-1} + \bv_l$</span>，<span class="mathjax-exps">$\av_l = h_l (\zv_l)$</span></p>
<br>
<p>整个网络：<span class="mathjax-exps">$\xv = \av_0 \xrightarrow{\Wv_1,\bv_1} \zv_1 \xrightarrow{h_1} \av_1 \xrightarrow{\Wv_2,\bv_2} \cdots \xrightarrow{\Wv_L,\bv_L} \zv_L \xrightarrow{h_L} \av_L = \hat{\yv}$</span></p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="100" class="slide " data-line="100" data-h="2" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>激活函数</h5></div></div>
<p>最早的 MP 模型采用阶跃函数<span class="mathjax-exps">$1_{z \geq 0}$</span>作为激活函数</p>
<br>
<p>改进方向：</p>
<ul>
<li>连续并几乎处处可导，可以高效计算</li>
<li>导数的值域在合适的范围内，否则影响用梯度下降进行训练</li>
</ul>
<br>
<p>常见的有</p>
<ul>
<li>Sigmoid 型：Logistic 函数，Tanh 函数</li>
<li>ReLU，带泄漏的 ReLU，带参数的 ReLU，ELU，Softplus</li>
<li>Swish 函数</li>
<li>Maxout 单元</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="124" class="slide " data-line="124" data-h="2" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Sigmoid 型</h5></div></div>
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<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="132" class="slide " data-line="132" data-h="2" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Logistic 函数</h5></div></div>
<p>将<span class="mathjax-exps">$\Rbb$</span><span class="blue">挤压</span>到<span class="mathjax-exps">$[0,1]$</span>，输出拥有概率意义：</p>
<p>

$$
\begin{align*}
    \sigma(z) = \frac{1}{1 + \exp (-z)} = \begin{cases}
        1 &amp; z \rightarrow \infty \\
        0 &amp; z \rightarrow -\infty
    \end{cases}
\end{align*}
$$
</p>

<br>
<p>Logistic 函数连续可导，在<span class="blue">零处导数最大</span></p>
<p>

$$
\begin{align*}
    \sigma'(z) &amp; = - \frac{- \exp (-z)}{(1 + \exp (-z))^2} = \frac{1}{1 + \exp (-z)} \frac{\exp (-z)}{1 + \exp (-z)} \\
    &amp; = \sigma(z) (1 - \sigma(z)) \leq \left( \frac{\sigma(z) + 1 - \sigma(z)}{2} \right)^2 \\
    &amp; = \frac{1}{4}
\end{align*}
$$
</p>

<p>均值不等式等号成立的条件是<span class="mathjax-exps">$\sigma(z) = 1 - \sigma(z)$</span>，即<span class="mathjax-exps">$z = 0$</span></p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="171" class="slide " data-line="171" data-h="2" data-v="3">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Tanh 函数</h5></div></div>
<p>将<span class="mathjax-exps">$\Rbb$</span><span class="blue">挤压</span>到<span class="mathjax-exps">$[-1,1]$</span>，<span class="blue">输出零中心化</span>，Logistic 函数的放大平移</p>
<p>

$$
\begin{align*}
    \tanh(z) &amp; = \frac{\exp(z) - \exp(-z)}{\exp(z) + \exp(-z)} = \frac{1 - \exp(-2z)}{1 + \exp(-2z)} \\
    &amp; = 2 \sigma(2z) - 1 \\
    &amp; = \begin{cases}
        1 &amp; z \rightarrow \infty \\
        -1 &amp; z \rightarrow -\infty
    \end{cases}
\end{align*}
$$
</p>

<br>
<p>性质：</p>
<ul>
<li>连续可导<span class="mathjax-exps">$\tanh'(z) = 2 (\sigma(2z))' = 4 \sigma(2z) (1 - \sigma(2z))$</span>，在<span class="mathjax-exps">$z = 0$</span>处导数最大</li>
<li>输出零中心化使得后一层的输入<span class="mathjax-exps">$\wv^\top \av + \bv$</span>也在零附近，而 Tanh 函数在零处导数最大，梯度下降更新效率较高，Logistic 函数输出恒为正，会进一步减慢梯度下降的收敛速度</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="201" class="slide " data-line="201" data-h="3" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>ReLU</h5></div></div>
<p>ReLU 全称叫修正线性单元 (<strong>re</strong>ctified <strong>l</strong>inear <strong>u</strong>nit)，定义为</p>
<p>

$$
\begin{align*}
    \relu(z) = \max \{ 0, z \} = \begin{cases}
        z &amp; z \geq 0 \\ 0 &amp; z &lt; 0
    \end{cases}
\end{align*}
$$
</p>

<br>
<p>优点</p>
<ul>
<li>计算只涉及加法、乘法和比较操作，非常高效</li>
<li>生物学解释：单侧抑制，宽兴奋边界，稀疏兴奋</li>
<li>在<span class="mathjax-exps">$z &gt; 0$</span>时导数恒为<span class="mathjax-exps">$1$</span>，缓解了<span class="blue">梯度消失</span>问题</li>
</ul>
<br>
<p>缺点</p>
<ul>
<li>输出非零中心化，对下一层不友好</li>
<li>死亡 ReLU 问题：对异常值特别敏感</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="236" class="slide " data-line="236" data-h="3" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>死亡 ReLU 问题</h5></div></div>
<p>由链式法则有</p>
<p>

$$
\begin{align*}
    \frac{\partial \relu(\wv^\top \xv + b)}{\partial \wv} &amp; = \frac{\partial \relu(\wv^\top \xv + b)}{\partial (\wv^\top \xv + b)} \frac{\partial (\wv^\top \xv + b)}{\partial \wv} \\
    &amp; = \frac{\partial \max \{ 0, \wv^\top \xv + b \}}{\partial (\wv^\top \xv + b)} \xv^\top \\
    &amp; = 1_{\wv^\top \xv + b \geq 0} \xv^\top
\end{align*}
$$
</p>

<p>如果第一个隐藏层中的某个神经元的权重向量<span class="mathjax-exps">$\wv$</span>初始化不当，使得对任意<span class="mathjax-exps">$\xv$</span>有<span class="mathjax-exps">$\wv^\top \xv + b &lt; 0$</span>，那么其关于<span class="mathjax-exps">$\wv$</span>的梯度将为零，在以后的训练过程中<span class="mathjax-exps">$\wv$</span>永远不会被更新</p>
<br>
<p>方案：带泄漏的 ReLU，带参数的 ReLU，ELU，Softplus</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="262" class="slide " data-line="262" data-h="3" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>ReLU 变体</h5></div></div>
<p>带泄漏的 ReLU：当<span class="mathjax-exps">$\wv^\top \xv + b &lt; 0$</span>时也有非零梯度</p>
<p>

$$
\begin{align*}
    \lrelu(z) &amp; = \begin{cases}
        z &amp; z \geq 0 \\ \gamma z &amp; z &lt; 0
    \end{cases} \\
    &amp; = \max \{ 0, z \} + \gamma \min \{ 0, z \} \overset{\gamma &lt; 1}{=} \max \{ z, \gamma z \}
\end{align*}
$$
</p>

<p>其中斜率<span class="mathjax-exps">$\gamma$</span>是一个很小的常数，比如<span class="mathjax-exps">$0.01$</span></p>
<br>
<p>带参数的 ReLU：斜率<span class="mathjax-exps">$\gamma_i$</span>可学习</p>
<p>

$$
\begin{align*}
    \prelu(z) &amp; = \begin{cases}
        z &amp; z \geq 0 \\ \gamma_i z &amp; z &lt; 0
    \end{cases} \\
    &amp; = \max \{ 0, z \} + \gamma_i \min \{ 0, z \}
\end{align*}
$$
</p>

<p>可以不同神经元具有不同的参数，也可以一组神经元共享一个参数</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="304" class="slide " data-line="304" data-h="3" data-v="3">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>ReLU 变体</h5></div></div>
<p>ELU 全称叫指数线性单元 (<strong>e</strong>xponential <strong>l</strong>inear <strong>u</strong>nit)</p>
<p>

$$
\begin{align*}
    \elu(z) &amp; = \begin{cases}
        z &amp; z \geq 0 \\ \gamma (\exp(z) - 1) &amp; z &lt; 0
    \end{cases} \\
    &amp; = \max \{ 0, z \} + \min \{ 0, \gamma (\exp(z) - 1) \}
\end{align*}
$$
</p>

<div class="bottom4"></div>
<p>Softplus 函数可以看作 ReLU 的平滑版本：</p>
<p>

$$
\begin{align*}
    \softplus(z) = \log (1 + \exp(z))
\end{align*}
$$
</p>

<p>其导数为 Logistic 函数</p>
<p>

$$
\begin{align*}
    \softplus'(z) = \frac{\exp(z)}{1 + \exp(z)} = \frac{1}{1 + \exp(-z)}
\end{align*}
$$
</p>

<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="351" class="slide " data-line="351" data-h="3" data-v="4">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>ReLU 族</h5></div></div>
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" class="width60 center top6">
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="359" class="slide " data-line="359" data-h="4" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Swish 函数</h5></div></div>
<p>Swish 函数是一种自门控 (self-gated) 激活函数：</p>
<p>

$$
\begin{align*}
    \swish(z) = z \cdot \sigma (\beta z) = \frac{z}{1 + \exp(-\beta z)}
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\beta$</span>是可学习的参数或一个固定超参数</p>
<ul>
<li>当<span class="mathjax-exps">$\sigma (\beta z)$</span>接近于<span class="mathjax-exps">$1$</span>时，门处于<span class="blue">开</span>状态，激活函数的输出近似于<span class="mathjax-exps">$z$</span>本身</li>
<li>当<span class="mathjax-exps">$\sigma (\beta z)$</span>接近于<span class="mathjax-exps">$0$</span>时，门处于<span class="blue">关</span>状态，激活函数的输出近似于<span class="mathjax-exps">$0$</span></li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="382" class="slide " data-line="382" data-h="4" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Swish 函数</h5></div></div>
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" class="width60 center top6">
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="390" class="slide " data-line="390" data-h="4" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>Maxout 单元</h5></div></div>
<p>考虑神经网络的第<span class="mathjax-exps">$l$</span>层：</p>
<p>

$$
\begin{align*}
    \zv_l &amp; = \Wv_l \av_{l-1} + \bv_l \\
    \av_l &amp; = h_l (\zv_l)
\end{align*}
$$
</p>

<p>前面提到的激活函数都是<span class="mathjax-exps">$\Rbb \mapsto \Rbb$</span>的，即<span class="mathjax-exps">$[\av_l]_i = h_l ([\zv_l]_i), ~ i \in [n_l]$</span></p>
<br>
<p>Maxout 单元是<span class="mathjax-exps">$\Rbb^{n_l} \mapsto \Rbb$</span>的，输入就是<span class="mathjax-exps">$\zv_l$</span>，其定义为</p>
<p>

$$
\begin{align*}
    \maxout (\zv) = \max_{k \in [K]} \{ \wv_k^\top \zv + b_k \}
\end{align*}
$$
</p>

<ul>
<li>整体学习输入到输出间的非线性关系</li>
<li><span class="mathjax-exps">$\relu(z) = \max \{ 0, z \}$</span>与<span class="mathjax-exps">$\lrelu(z) \overset{\gamma &lt; 1}{=} \max \{ z, \gamma z \}$</span>都是 Maxout 单元的特例</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="428" class="slide " data-line="428" data-h="5" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>应用到机器学习</h5></div></div>
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<p>前<span class="mathjax-exps">$L-1$</span>层是复合函数<span class="mathjax-exps">$\psi: \Rbb^d \mapsto \Rbb^{n_{L-1}}$</span>，可以看作一种特征变换方法</p>
<br>
<p>最后一层是学习器<span class="mathjax-exps">$\hat{\yv} = g(\psi(\xv); \Wv_L, \bv_L)$</span>，对输入<span class="mathjax-exps">$\psi(\xv)$</span>进行预测</p>
<ul>
<li>若<span class="mathjax-exps">$y \in \{ \pm 1 \}$</span>，最后一层只需<span class="mathjax-exps">$1$</span>个神经元，并采用 Logistic 激活函数</li>
<li>若<span class="mathjax-exps">$y \in [C]$</span>，最后一层需<span class="mathjax-exps">$C$</span>个神经元，并采用 Softmax 激活函数</li>
</ul>
<br>
<p>因此对数几率回归也可看作只有一层 (没有隐藏层) 的神经网络</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="451" class="slide " data-line="451" data-h="5" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>深度学习</h5></div></div>
<p>传统机器学习：特征处理和学习两阶段分开进行</p>
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<p>深度学习：特征工程和模型学习合二为一，端到端 (end-to-end)</p>
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</p><div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="467" class="slide " data-line="467" data-h="5" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>用 tensorflow 实现</h5></div></div>
<pre data-role="codeBlock" data-info="python {.line-numbers}" class="language-python line-numbers"><span class="token keyword">from</span> sklearn<span class="token punctuation">.</span>datasets <span class="token keyword">import</span> load_breast_cancer
<span class="token keyword">from</span> sklearn<span class="token punctuation">.</span>model_selection <span class="token keyword">import</span> train_test_split
<span class="token keyword">from</span> tensorflow<span class="token punctuation">.</span>keras<span class="token punctuation">.</span>layers <span class="token keyword">import</span> Dense
<span class="token keyword">from</span> tensorflow<span class="token punctuation">.</span>keras<span class="token punctuation">.</span>models <span class="token keyword">import</span> Sequential
<span class="token keyword">from</span> tensorflow<span class="token punctuation">.</span>keras<span class="token punctuation">.</span>optimizers <span class="token keyword">import</span> SGD

X<span class="token punctuation">,</span> y <span class="token operator">=</span> load_breast_cancer<span class="token punctuation">(</span>return_X_y<span class="token operator">=</span><span class="token boolean">True</span><span class="token punctuation">)</span>
X_train<span class="token punctuation">,</span> X_test<span class="token punctuation">,</span> y_train<span class="token punctuation">,</span> y_test <span class="token operator">=</span> train_test_split<span class="token punctuation">(</span>X<span class="token punctuation">,</span> y<span class="token punctuation">,</span> random_state<span class="token operator">=</span><span class="token number">0</span><span class="token punctuation">)</span>

model <span class="token operator">=</span> Sequential<span class="token punctuation">(</span><span class="token punctuation">)</span>
model<span class="token punctuation">.</span>add<span class="token punctuation">(</span>Dense<span class="token punctuation">(</span><span class="token number">64</span><span class="token punctuation">,</span> activation<span class="token operator">=</span><span class="token string">"relu"</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
model<span class="token punctuation">.</span>add<span class="token punctuation">(</span>Dense<span class="token punctuation">(</span><span class="token number">64</span><span class="token punctuation">,</span> activation<span class="token operator">=</span><span class="token string">"relu"</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
model<span class="token punctuation">.</span>add<span class="token punctuation">(</span>Dense<span class="token punctuation">(</span><span class="token number">1</span><span class="token punctuation">,</span> activation<span class="token operator">=</span><span class="token string">"sigmoid"</span><span class="token punctuation">)</span><span class="token punctuation">)</span>
model<span class="token punctuation">.</span><span class="token builtin">compile</span><span class="token punctuation">(</span>optimizer<span class="token operator">=</span>SGD<span class="token punctuation">(</span><span class="token number">0.001</span><span class="token punctuation">)</span><span class="token punctuation">,</span>
              loss<span class="token operator">=</span><span class="token string">"binary_crossentropy"</span><span class="token punctuation">,</span>
              metrics<span class="token operator">=</span><span class="token punctuation">[</span><span class="token string">"accuracy"</span><span class="token punctuation">]</span><span class="token punctuation">,</span>
              <span class="token punctuation">)</span>

model<span class="token punctuation">.</span>fit<span class="token punctuation">(</span>X_train<span class="token punctuation">,</span> y_train<span class="token punctuation">,</span> epochs<span class="token operator">=</span><span class="token number">10</span><span class="token punctuation">)</span>
model<span class="token punctuation">.</span>evaluate<span class="token punctuation">(</span>X_test<span class="token punctuation">,</span> y_test<span class="token punctuation">,</span> verbose<span class="token operator">=</span><span class="token number">2</span><span class="token punctuation">)</span>

Epoch <span class="token number">1</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 1s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">51.0188</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.4906</span>
Epoch <span class="token number">2</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">1.0154</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.7465</span>
Epoch <span class="token number">3</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.5027</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8146</span>
Epoch <span class="token number">4</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.4219</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8239</span>
Epoch <span class="token number">5</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.4142</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8380</span>
Epoch <span class="token number">6</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.3101</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8779</span>
Epoch <span class="token number">7</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.2744</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8944</span>
Epoch <span class="token number">8</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.2454</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.9061</span>
Epoch <span class="token number">9</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.3001</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8897</span>
Epoch <span class="token number">10</span><span class="token operator">/</span><span class="token number">10</span>
<span class="token number">14</span><span class="token operator">/</span><span class="token number">14</span> <span class="token punctuation">[</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token operator">==</span><span class="token punctuation">]</span> <span class="token operator">-</span> 0s 1ms<span class="token operator">/</span>step <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.2557</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.8991</span>

<span class="token number">5</span><span class="token operator">/</span><span class="token number">5</span> <span class="token operator">-</span> 0s <span class="token operator">-</span> loss<span class="token punctuation">:</span> <span class="token number">0.2264</span> <span class="token operator">-</span> accuracy<span class="token punctuation">:</span> <span class="token number">0.9231</span>
<span aria-hidden="true" class="line-numbers-rows"><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span><span></span></span></pre><div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section data-notes="" lineno="519" class="slide " data-line="519" data-h="6" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>求解参数</h5></div></div>
<p>设采用交叉熵损失，对样本<span class="mathjax-exps">$(\xv, y)$</span>，损失函数为<span class="mathjax-exps">$\Lcal (\yv, \hat{\yv}) = - \yv \log \hat{\yv}$</span></p>
<br>
<p>优化目标为</p>
<p>

$$
\begin{align*}
    \min_{\Wv, \bv} ~ \frac{1}{2} \| \Wv \|_F^2 + \frac{\lambda}{m} \sum_{i \in [m]} \Lcal (\yv_i, \hat{\yv}_i)
\end{align*}
$$
</p>

<br>
<p>梯度下降 (标量对某矩阵求导的结果的尺寸与该矩阵呈转置关系)</p>
<p>

$$
\begin{align*}
    \Wv &amp; ~ \leftarrow ~ \Wv - \eta \left( \frac{\lambda}{m} \sum_{i \in [m]} \class{yellow}{\frac{\partial \Lcal (\yv_i, \hat{\yv}_i)}{\partial \Wv^\top}} + \Wv \right) \\
    \bv &amp; ~ \leftarrow ~ \bv - \eta \cdot \frac{\lambda}{m} \sum_{i \in [m]} \class{yellow}{\frac{\partial \Lcal (\yv_i, \hat{\yv}_i)}{\partial \bv^\top}}
\end{align*}
$$
</p>

<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section><section data-notes="" lineno="556" class="slide " data-line="556" data-h="7" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>求解参数</h5></div></div>
<p>整个网络：<span class="mathjax-exps">$\xv = \av_0 \xrightarrow{\Wv_1,\bv_1} \zv_1 \xrightarrow{h_1} \av_1 \xrightarrow{\Wv_2,\bv_2} \cdots \xrightarrow{\Wv_L,\bv_L} \zv_L \xrightarrow{h_L} \av_L = \hat{\yv}$</span></p>
<br>
<p>损失<span class="mathjax-exps">$\Lcal (\yv, \hat{\yv})$</span>的计算为<span class="blue">正向传播</span></p>
<ul>
<li>样本从输入层进入，经隐藏层逐层传播到最后输出层</li>
<li><span class="mathjax-exps">$\hat{\yv} = \av_L = h_L (\zv_L)$</span>是对样本<span class="mathjax-exps">$\xv$</span>的预测，据此计算<span class="mathjax-exps">$\Lcal (\yv, \hat{\yv}) = \Lcal (\yv, h_L (\zv_L))$</span></li>
</ul>
<br>
<p>先看最后一层<span class="mathjax-exps">$\zv_L = \Wv_L ~ \av_{L-1} + \bv_L$</span>，<span class="mathjax-exps">$\av_L = h_L (\zv_L)$</span>，由<span class="blue">链式法则</span> (?) 有</p>
<p>

$$
\begin{align*}
    \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \bv_L} &amp; = \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \zv_L} \frac{\partial \zv_L}{\partial \bv_L} = \deltav_L^\top \frac{\partial \zv_L}{\partial \bv_L} \\
    \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \Wv_L} &amp; = \sum_{j \in [n_L]} \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial [\zv_L]_j} \frac{\partial [\zv_L]_j}{\partial \Wv_L} = \sum_{j \in [n_L]} [\deltav_L]_j \frac{\partial [\zv_L]_j}{\partial \Wv_L}
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\deltav_L^\top = \partial \Lcal (\yv, \hat{\yv}) / \partial \zv_L \in \Rbb^{n_L}$</span>为第<span class="mathjax-exps">$L$</span>层的<span class="blue">误差项</span>，该项可直接求解</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="588" class="slide " data-line="588" data-h="7" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>求解参数 反向传播</h5></div></div>
<p>整个网络：<span class="mathjax-exps">$\xv = \av_0 \xrightarrow{\Wv_1,\bv_1} \zv_1 \xrightarrow{h_1} \av_1 \xrightarrow{\Wv_2,\bv_2} \cdots \xrightarrow{\Wv_L,\bv_L} \zv_L \xrightarrow{h_L} \av_L = \hat{\yv}$</span></p>
<br>
<p>类似的对第<span class="mathjax-exps">$l$</span>层<span class="mathjax-exps">$\zv_l = \Wv_l \av_{l-1} + \bv_l$</span>，<span class="mathjax-exps">$\av_l = h_l (\zv_l)$</span>，由<span class="blue">链式法则</span> (?) 有</p>
<p>

$$
\begin{align*}
    \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \bv_l} = \deltav_l^\top \frac{\partial \zv_l}{\partial \bv_l}, \quad \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \Wv_l} = \sum_{j \in [n_l]} [\deltav_l]_j \frac{\partial [\zv_l]_j}{\partial \Wv_l}
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\deltav_l^\top = \partial \Lcal (\yv, \hat{\yv}) / \partial \zv_l \in \Rbb^{n_l}$</span>为第<span class="mathjax-exps">$l$</span>层的<span class="blue">误差项</span></p>
<br>
<p>误差<span class="blue">反向传播</span> (<strong>b</strong>ack<strong>p</strong>ropagation, BP)：前一层的误差可由后一层得到</p>
<p>

$$
\begin{align*}
    \deltav_{l-1}^\top = \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \zv_{l-1}} = \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \zv_l} \frac{\partial \zv_l}{\partial \av_{l-1}} \frac{\partial \av_{l-1}}{\partial \zv_{l-1}} = \deltav_l^\top \frac{\partial \zv_l}{\partial \av_{l-1}} \frac{\partial h_{l-1}(\zv_{l-1})}{\partial \zv_{l-1}}
\end{align*}
$$
</p>

<p>对第<span class="mathjax-exps">$l$</span>层<span class="mathjax-exps">$\zv_l = \Wv_l \av_{l-1} + \bv_l$</span>，如何求<span class="mathjax-exps">$\partial \zv_l / \partial \av_{l-1}$</span>、<span class="mathjax-exps">$\partial \zv_l / \partial \bv_l$</span>、<span class="mathjax-exps">$\partial [\zv_l]_j / \partial \Wv_l$</span></p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="628" class="slide " data-line="628" data-h="7" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>求解参数 反向传播</h5></div></div>
<p>对<span class="mathjax-exps">$\zv = \Wv \av + \bv$</span>，如何求<span class="mathjax-exps">$\partial \zv / \partial \av$</span>、<span class="mathjax-exps">$\partial \zv / \partial \bv$</span>、<span class="mathjax-exps">$\partial z_j / \partial \Wv$</span></p>
<p>由矩阵求导公式易知</p>
<p>

$$
\begin{align*}
    \frac{\partial \zv}{\partial \av} = \frac{\partial (\Wv \av)}{\partial \av} = \Wv, \quad \frac{\partial \zv}{\partial \bv} = \frac{\partial \bv}{\partial \bv} = \Iv
\end{align*}
$$
</p>

<p>注意<span class="mathjax-exps">$z_j = \sum_k w_{jk} a_k + b_k$</span>只与<span class="mathjax-exps">$\Wv$</span>的第<span class="mathjax-exps">$j$</span>行有关，于是</p>
<p>

$$
\begin{align*}
    \frac{\partial z_j}{\partial \Wv} = \underbrace{\begin{bmatrix} \zerov, \ldots, \av, \ldots, \zerov \end{bmatrix}}_{\text{only }\av\text{ at }j\text{-th column}} = \av \ev_j^\top
\end{align*}
$$
</p>

<p>从而</p>
<p>

$$
\begin{align*}
    \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \Wv_l} = \sum_{j \in [n_l]} [\deltav_l]_j \frac{\partial [\zv_l]_j}{\partial \Wv_l} = \av_{l-1} \sum_{j \in [n_l]} [\deltav_l]_j \ev_j^\top = \av_{l-1} \deltav_l^\top
\end{align*}
$$
</p>

<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="672" class="slide " data-line="672" data-h="7" data-v="3">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>反向传播算法</h5></div></div>
<p>输入：训练集<span class="mathjax-exps">$\Scal$</span>，验证集<span class="mathjax-exps">$\Vcal$</span>，以及相关超参数</p>
<ol>
<li>随机初始化<span class="mathjax-exps">$\Wv$</span>和<span class="mathjax-exps">$\bv$</span></li>
<li>repeat</li>
<li>  对训练集<span class="mathjax-exps">$\Scal$</span>中的样本随机重排序</li>
<li>  for <span class="mathjax-exps">$i = 1, \ldots, m$</span> do</li>
<li>    获取样本<span class="mathjax-exps">$(\xv_i, y_i)$</span></li>
<li>    前向传播，计算每一层的<span class="mathjax-exps">$\zv_l = \Wv_l \av_{l-1} + \bv_l$</span>，直到最后一层</li>
<li>    反向传播计算每一层的误差项<span class="mathjax-exps">$\deltav_l^\top = \deltav_{l+1}^\top \Wv_{l+1} \diag (h_l'(\zv_l))$</span></li>
<li>    计算梯度<span class="mathjax-exps">$\partial \Lcal / \partial \Wv_l = \av_{l-1} \deltav_l^\top$</span>、<span class="mathjax-exps">$\partial \Lcal / \partial \bv_l = \deltav_l^\top$</span></li>
<li>    采用梯度下降更新<span class="mathjax-exps">$\Wv_l$</span>和<span class="mathjax-exps">$\bv_l$</span></li>
<li>  end</li>
<li>until 神经网络模型在验证集<span class="mathjax-exps">$\Vcal$</span>上的错误率不再下降</li>
</ol>
<p>输出：<span class="mathjax-exps">$\Wv$</span>和<span class="mathjax-exps">$\bv$</span></p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="694" class="slide " data-line="694" data-h="8" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>梯度消失</h5></div></div>
<p>神经网络中误差反向传播的迭代公式为</p>
<p>

$$
\begin{align*}
    \deltav_l^\top = \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \zv_l} = \frac{\partial \Lcal (\yv, \hat{\yv})}{\partial \zv_{l+1}} \frac{\partial \zv_{l+1}}{\partial \av_l} \frac{\partial \av_l}{\partial \zv_l} = \deltav_{l+1}^\top \Wv_{l+1} \diag (h_l'(\zv_l))
\end{align*}
$$
</p>

<br>
<p>对于 Sigmoid 型激活函数</p>
<ul>
<li><span class="mathjax-exps">$\sigma'(z) = \sigma(z) (1 - \sigma(z)) \leq \frac{1}{4}$</span></li>
<li><span class="mathjax-exps">$\tanh'(z) = 4 \sigma(2z) (1 - \sigma(2z)) \leq 4 \cdot \frac{1}{4} = 1$</span></li>
</ul>
<br>
<p>误差每传播一层都会乘以一个小于等于<span class="mathjax-exps">$1$</span>的系数，当网络层数很深时，梯度会不断衰减甚至消失，使得整个网络很难训练</p>
<br>
<p>方案：使用导数比较大的激活函数，比如 ReLU</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="727" class="slide " data-line="727" data-h="8" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>残差网络</h5></div></div>
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<p>残差模块：<span class="mathjax-exps">$\zv_l = \av_{l-1} + \class{yellow}{\Uv_2 \cdot h(\Uv_1 \cdot \av_{l-1} + \cv_1) + \cv_2} = \av_{l-1} + \class{yellow}{f(\av_{l-1})}$</span></p>
<p>假设<span class="mathjax-exps">$\av_l = \zv_l$</span>，即残差模块输出不使用激活函数，对<span class="mathjax-exps">$\forall t \in [l]$</span>有</p>
<p>

$$
\begin{align*}
    \av_l &amp; = \av_{l-1} + f(\av_{l-1}) = \av_{l-2} + f(\av_{l-2}) + f(\av_{l-1}) \\
    &amp; = \cdots = \av_{l-t} + \sum_{i=l-t}^{l-1} f(\av_i)
\end{align*}
$$
</p>

<p>低层输入可以<span class="blue">恒等</span>传播到任意高层</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="752" class="slide " data-line="752" data-h="8" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>残差网络</h5></div></div>
<p>

$$
\begin{align*}
    \av_l = \av_{l-t} + \sum_{i=l-t}^{l-1} f(\av_i)
\end{align*}
$$
</p>

<p>由链式法则有</p>
<p>

$$
\begin{align*}
    \frac{\partial \Lcal}{\partial \av_{l-t}} &amp; = \frac{\partial \Lcal}{\partial \av_l} \frac{\partial \av_l}{\partial \av_{l-t}} = \frac{\partial \Lcal}{\partial \av_l} \left( \frac{\partial \av_{l-t}}{\partial \av_{l-t}} + \frac{\partial }{\partial \av_{l-t}} \sum_{i=l-t}^{l-1} f(\av_i) \right) \\
    &amp; = \frac{\partial \Lcal}{\partial \av_l} \left( \Iv + \frac{\partial }{\partial \av_{l-t}} \sum_{i=l-t}^{l-1} f(\av_i) \right) \\
    &amp; = \frac{\partial \Lcal}{\partial \av_l} + \frac{\partial \Lcal}{\partial \av_l} \left( \frac{\partial }{\partial \av_{l-t}} \sum_{i=l-t}^{l-1} f(\av_i) \right)
\end{align*}
$$
</p>

<p>高层误差可以<span class="blue">恒等</span>传播到任意低层，梯度消失得以缓解</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="784" class="slide " data-line="784" data-h="9" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>优化算法</h5></div></div>
<p>神经网络通常采用小批量梯度下降：</p>
<p>

$$
\begin{align*}
    \Wv^\top ~ \leftarrow ~ \Wv^\top - \frac{\eta}{|\Bcal|} \sum_{i \in \Bcal} \frac{\partial \Lcal (\yv_i, \hat{\yv}_i)}{\partial \Wv}
\end{align*}
$$
</p>

<p>批量大小<span class="mathjax-exps">$|\Bcal|$</span>不影响随机梯度的期望，但会影响方差</p>
<ul>
<li><span class="mathjax-exps">$|\Bcal|$</span>越大，方差越小，训练越稳定，可以采用较大的步长加快收敛</li>
<li><span class="mathjax-exps">$|\Bcal|$</span>越小，方差越大，需采用较小的步长，否则可能不收敛</li>
</ul>
<br>
<p>线性缩放规则：<span class="mathjax-exps">$|\Bcal|$</span>增加<span class="mathjax-exps">$k$</span>倍，步长也增加<span class="mathjax-exps">$k$</span>倍，但当<span class="mathjax-exps">$|\Bcal|$</span>特别大时，线性缩放也还是会出现训练不稳定</p>
<ul>
<li>步长调整</li>
<li>更新方向调整</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="814" class="slide " data-line="814" data-h="9" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>步长调整</h5></div></div>
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<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>步长衰减</h5></div></div>
<p>基本想法：在一开始要使用大步长来保证收敛速度，在接近最优解时要用小步长避免来回震荡，也称为步长退火</p>
<br>
<p>设初始步长为<span class="mathjax-exps">$\eta_0$</span>，第<span class="mathjax-exps">$t$</span>次迭代时的步长为<span class="mathjax-exps">$\eta_t$</span>，常见的衰减方式为根据迭代次数进行衰减</p>
<ul>
<li>分段常数衰减：每经过<span class="mathjax-exps">$T_1, T_2, \ldots, T_n$</span>次迭代步长衰减为原来的<span class="mathjax-exps">$\beta_1, \beta_2, \ldots, \beta_n$</span>倍，其中<span class="mathjax-exps">$T_n$</span>和<span class="mathjax-exps">$\beta_n &lt; 1$</span>为超参数</li>
<li>逆时衰减：<span class="mathjax-exps">$\eta_t = \eta_0 / (1 + \beta * t)$</span>，其中<span class="mathjax-exps">$\beta$</span>为衰减率</li>
<li>指数衰减：<span class="mathjax-exps">$\eta_t = \eta_0 \beta^t$</span>，其中<span class="mathjax-exps">$\beta &lt; 1$</span>为衰减率</li>
<li>自然指数衰减：<span class="mathjax-exps">$\eta_t = \eta_0 \exp(-\beta * t)$</span></li>
<li>余弦衰减：<span class="mathjax-exps">$\eta_t = \eta_0 (1 + \cos (t \pi /T) )$</span>，其中<span class="mathjax-exps">$T$</span>为总的迭代次数</li>
</ul>
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<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>步长预热</h5></div></div>
<p>在训练初始阶段，由于参数是随机初始化的，梯度往往也比较大，如果初始步长也很大，会使得训练不稳定</p>
<br>
<p>步长预热：</p>
<ul>
<li>在最初几轮迭代时，采用较小的步长</li>
<li>等梯度下降到一定程度后再恢复为初始步长</li>
</ul>
<br>
<p>假设预热迭代次数为<span class="mathjax-exps">$T'$</span>，初始步长为<span class="mathjax-exps">$\eta_0$</span>，则</p>
<p>

$$
\begin{align*}
    \eta'_t = \frac{t}{T'} \eta_0, \quad t \in [T']
\end{align*}
$$
</p>

<p>当预热过程结束，再选择一种步长衰减方法来逐渐降低步长</p>
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</section><section vertical="true" data-notes="" lineno="871" class="slide " data-line="871" data-h="10" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>周期性步长</h5></div></div>
<p>在训练过程中周期性地增大步长</p>
<br>
<p>目的：在训练过程中有助于逃离<span class="blue">尖锐的局部最小值点</span>和<span class="blue">鞍点</span></p>
<ul>
<li>平坦的局部最小值点：鲁棒性好，微小的参数变动不会对模型有剧烈影响</li>
<li>尖锐的局部最小值点：鲁棒性差，微小的参数变动也会导致模型剧烈变化</li>
</ul>
<br>
<p>周期性地增大步长虽然会短期内会损害优化过程，但通常最终会收敛到更加理想的局部极小值点，类似于模拟退火</p>
<ul>
<li><span class="blue">循环步长</span>：每个循环周期的长度都为<span class="mathjax-exps">$2 \Delta T$</span>，前<span class="mathjax-exps">$\Delta T$</span>轮步长线性增大，后<span class="mathjax-exps">$\Delta T$</span>轮步长线性缩小，第<span class="mathjax-exps">$n$</span>个周期中步长的上界和下界随着<span class="mathjax-exps">$m$</span>的增大而逐渐减小</li>
<li><span class="blue">带热重启的 SGD</span>：步长每隔一定周期后重新初始化为某个预先设定的值，然后逐渐衰减；每次重启后模型参数不是从头开始优化，而是在重启前的参数基础上继续优化</li>
</ul>
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</section></section><section><section data-notes="" lineno="893" class="slide " data-line="893" data-h="11" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>自适应步长</h5></div></div>
<p>AdaGrad：第<span class="mathjax-exps">$t$</span>轮迭代先计算每个参数<span class="blue">梯度平方的累积值</span>：</p>
<p>

$$
\begin{align*}
    \Gv_t = \sum_{\tau \in [t]} \gv_\tau \odot \gv_\tau
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\odot$</span>为按元素乘积，<span class="mathjax-exps">$\gv_\tau \in \Rbb^{|\Wv|}$</span>是第<span class="mathjax-exps">$\tau$</span>次迭代时的梯度</p>
<br>
<p>再利用累积梯度平方做衰减 (每个元素各自计算)</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} \leftarrow \Wv_t - \frac{\alpha}{\sqrt{\Gv_t + \epsilon}} \odot \gv_\tau
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\alpha$</span>是初始步长，<span class="mathjax-exps">$\epsilon$</span>是为了保持数值稳定而设置的非常小的常数</p>
<br>
<p>缺点：经过一定轮数的迭代仍未找到最优点时，由于此时步长已经非常小，很难再继续找到最优点</p>
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<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>自适应步长</h5></div></div>
<p>RMSprop：第<span class="mathjax-exps">$t$</span>轮迭代先计算<span class="blue">梯度平方的指数衰减移动平均</span>：</p>
<p>

$$
\begin{align*}
    \Gv_t = \beta \Gv_{t-1} + (1 - \beta) \gv_\tau \odot \gv_\tau = (1 - \beta) \sum_{\tau \in [t]} \beta^{t - \tau} \gv_\tau \odot \gv_\tau
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\beta &lt; 1$</span>为衰减率，一般取值<span class="mathjax-exps">$0.9$</span></p>
<br>
<p>RMSprop 的更新公式为</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} \leftarrow \Wv_t - \frac{\alpha}{\sqrt{\Gv_t + \epsilon}} \odot \gv_\tau
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\alpha$</span>通常设为<span class="mathjax-exps">$0.001$</span></p>
<br>
<p>较 AdaGrad 的优点：<span class="mathjax-exps">$\Gv_t$</span>并非单调增加，故步长不是单调衰减，既可以变小也可以变大</p>
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</section><section vertical="true" data-notes="" lineno="973" class="slide " data-line="973" data-h="11" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>自适应步长</h5></div></div>
<p>AdaDelta：进一步引入<span class="blue">参数更新差平方的指数衰减移动平均</span>：</p>
<p>

$$
\begin{align*}
    \Delta \Uv_{t-1}^2 = \beta \Delta \Uv_{t-2}^2 + (1 - \beta) \Delta \Wv_{t-1} \odot \Delta \Wv_{t-1}
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\beta$</span>为衰减率，<span class="mathjax-exps">$\Delta \Wv_{t-1} = \Wv_t - \Wv_{t-1}$</span>为参数更新差</p>
<div class="bottom4"></div>
<p>AdaDelta 的更新公式为</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} \leftarrow \Wv_t - \frac{\sqrt{\Delta \Uv_{t-1}^2 + \epsilon}}{\sqrt{\Gv_t + \epsilon}} \odot \gv_\tau
\end{align*}
$$
</p>

<p>优点：将 RMSprop 中的初始步长<span class="mathjax-exps">$\alpha$</span>改为动态计算的<span class="mathjax-exps">$\Delta \Uv_{t-1}$</span>，在一定程度上抑制了步长的波动</p>
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</section></section><section><section data-notes="" lineno="1009" class="slide " data-line="1009" data-h="12" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>更新方向调整</h5></div></div>
<p>动量法：计算负梯度的“加权移动平均”作为参数的更新方向</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} - \Wv_t = \Delta \Wv_t = \rho \Delta \Wv_{t-1} - \alpha \gv_t = - \alpha \sum_{\tau \in [t]} \rho^{t - \tau} \gv_\tau
\end{align*}
$$
</p>

<p>Nesterov 加速梯度：改进动量法的第二步</p>
<p>

$$
\begin{align*}
    \begin{cases} \widetilde{\Wv} = \Wv_t + \rho \Delta \Wv_{t-1} \\ \Wv_{t+1} = \widetilde{\Wv} - \alpha ~ \class{yellow}{\gv_t (\Wv_t)} \end{cases}
    ~ \Longrightarrow ~
    \begin{cases} \widetilde{\Wv} = \Wv_t + \rho \Delta \Wv_{t-1} \\ \Wv_{t+1} = \widetilde{\Wv} - \alpha ~ \class{yellow}{\gv_t (\widetilde{\Wv})} \end{cases}
\end{align*}
$$
</p>

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</section><section vertical="true" data-notes="" lineno="1041" class="slide " data-line="1041" data-h="12" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>更新方向调整</h5></div></div>
<p>Adam (<strong>ada</strong>ptive <strong>m</strong>oment estimation)：动量法和 RMSprop 的结合</p>
<p>

$$
\begin{align*}
    \Mv_t &amp; = \beta_1 \Mv_{t-1} + (1 - \beta_1) \gv_t = (1 - \beta_1) \sum_{\tau \in [t]} \beta_1^{t - \tau} \gv_\tau \\
    \Gv_t &amp; = \beta_2 \Gv_{t-1} + (1 - \beta_2) \gv_t \odot \gv_t = (1 - \beta_2) \sum_{\tau \in [t]} \beta_2^{t - \tau} \gv_\tau \odot \gv_\tau
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\beta_1$</span>、<span class="mathjax-exps">$\beta_2$</span>为衰减率，一般取值<span class="mathjax-exps">$\beta_1 = 0.9$</span>、<span class="mathjax-exps">$\beta_2 = 0.99$</span></p>
<br>
<p>

$$
\begin{align*}
    \Ebb [\Mv_t] &amp; = (1 - \beta_1) \sum_{\tau \in [t]} \beta_1^{t - \tau} \Ebb [\gv_\tau] = (1 - \beta_1^t) \Ebb [\gv_\tau] \\
    \Ebb [\Gv_t] &amp; = (1 - \beta_2) \sum_{\tau \in [t]} \beta_2^{t - \tau} \Ebb [\gv_\tau \odot \gv_\tau] = (1 - \beta_2^t) \Ebb [\gv_\tau \odot \gv_\tau]
\end{align*}
$$
</p>

<p>因此<span class="mathjax-exps">$\widetilde{\Mv}_t = \Mv_t / (1 - \beta_1^t)$</span>可以看作一阶矩，<span class="mathjax-exps">$\widetilde{\Gv}_t = \Gv_t / (1 - \beta_2^t)$</span>为二阶矩</p>
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</section><section vertical="true" data-notes="" lineno="1077" class="slide " data-line="1077" data-h="12" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>更新方向调整</h5></div></div>
<p>Adam 的更新公式为</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} \leftarrow \Wv_t - \frac{\alpha}{\sqrt{\widetilde{\Gv}_t + \epsilon}} \odot \widetilde{\Mv}_t
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$\alpha$</span>通常设为<span class="mathjax-exps">$0.001$</span>，也可以进行衰减，例如<span class="mathjax-exps">$\alpha_t = \alpha / \sqrt{t}$</span></p>
<br>
<p>如果将 NAG 和 RMSprop 的结合，则得到 Nadam</p>
<br>
<p>对于深层网络，在基于梯度下降的训练过程中，除了梯度消失，也会出现梯度爆炸，此时可进行梯度截断</p>
<ul>
<li>按值截断：<span class="mathjax-exps">$\gv_t = \max \{ \min \{ \gv_t, b \}, a \}$</span></li>
<li>按范数截断：<span class="mathjax-exps">$\gv_t = b ~ \gv_t / \| \gv_t \|$</span></li>
</ul>
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</section></section><section><section data-notes="" lineno="1108" class="slide " data-line="1108" data-h="13" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>训练技巧</h5></div></div>
<p>训练神经网络有很多奇技淫巧</p>
<ul>
<li>参数初始化</li>
<li>逐层归一化</li>
<li>超参数选择</li>
<li>权重衰减</li>
<li>提前停止</li>
<li>随机丢弃</li>
<li>数据增强</li>
<li>Mixup</li>
</ul>
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</section><section vertical="true" data-notes="" lineno="1125" class="slide " data-line="1125" data-h="13" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>参数初始化</h5></div></div>
<p>感知机、支持向量机、对数几率回归的<span class="mathjax-exps">$\wv$</span>通常初始化为零</p>
<br>
<p>神经网络的<span class="mathjax-exps">$\Wv$</span>如果全部初始化为零，在第一遍前向计算时，所有的隐层神经元的激活值都相同，这样会导致深层神经元没有区分性</p>
<br>
<p>方案：随机初始化</p>
<br>
<p>策略：<span class="blue">保持每个神经元输入和输出的方差一致</span></p>
<br>
<p>第<span class="mathjax-exps">$l$</span>个隐层的神经元<span class="mathjax-exps">$z$</span>接受前一层的输出<span class="mathjax-exps">$a_1, \ldots, a_{n_{l-1}}$</span>作为输入</p>
<p>

$$
\begin{align*}
    z = \sum_{i \in [n_{l-1}]} w_i a_i
\end{align*}
$$
</p>

<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1159" class="slide " data-line="1159" data-h="13" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>参数初始化</h5></div></div>
<p>假设<span class="mathjax-exps">$w_i$</span>和<span class="mathjax-exps">$a_i$</span>的均值都为<span class="mathjax-exps">$0$</span>，并且互相独立，则<span class="mathjax-exps">$z$</span>的方差为</p>
<p>

$$
\begin{align*}
    \var[z] = \sum_{i \in [n_{l-1}]} \var[w_i] \var[a_i] = n_{l-1} \var[w_i] \var[a_i]
\end{align*}
$$
</p>

<br>
<p>若想保持每个神经元的输入和输出的方差一致，则有<span class="mathjax-exps">$\var[w_i] = 1 / n_{l-1}$</span></p>
<br>
<p>同理在反向传播中，若想误差信号也不被放大或缩小，需将<span class="mathjax-exps">$w_i$</span>的方差保持为<span class="mathjax-exps">$\var[w_i] = 1 / n_l$</span></p>
<br>
<p>两相折中，可以设置<span class="mathjax-exps">$\var[w_i] = 2 / (n_l + n_{l-1})$</span></p>
<ul>
<li><span class="blue">正态分布初始化</span>，<span class="mathjax-exps">$\Ncal (0, \sqrt{2 / (n_l + n_{l-1})})$</span></li>
<li><span class="blue">均匀分布初始化</span>，若分布区间为<span class="mathjax-exps">$[-r,r]$</span>，则<span class="mathjax-exps">$r = \sqrt{6 / (n_l + n_{l-1})}$</span></li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="1192" class="slide " data-line="1192" data-h="14" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>逐层归一化</h5></div></div>
<p>在使用随机梯度下降来训练网络时</p>
<ul>
<li>每次参数更新都会导致网络中间每一层的输入的分布发生改变</li>
<li>越深的层的输入分布会改变得越明显</li>
</ul>
<p>如果某个神经层的输入分布发生了改变，那么其参数需要重新学习</p>
<p><span class="blue">批量归一化</span> (<strong>b</strong>atch <strong>n</strong>ormalization, BN)：逐层将各个神经元<span class="mathjax-exps">$z$</span>归一化到标准正态分布</p>
<p>

$$
\begin{align*}
    \hat{z} = \frac{z - \Ebb[z]}{\sqrt{\var [z] + \epsilon}}
\end{align*}
$$
</p>

<p><span class="mathjax-exps">$z$</span>的期望和方差通常用当前小批量样本集的均值和方差近似估计</p>
<p>批量归一化操作可以看作一个特殊的层，加在每一层非线性激活函数前：<span class="mathjax-exps">$\av_{l+1} = h(\mathrm{BN} (\Wv \av_l))$</span></p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1221" class="slide " data-line="1221" data-h="14" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>逐层归一化</h5></div></div>
<p>批量归一化：针对单个神经元</p>
<ul>
<li>要求小批量样本数不能太小，否则难以得到单个神经元较准确的统计信息</li>
</ul>
<p>层归一化：针对一层的所有神经元</p>
<br>
<p>设小批量样本数为<span class="mathjax-exps">$k$</span>，该层神经元数为<span class="mathjax-exps">$n$</span></p>
<p>

$$
\begin{align*}
    \begin{bmatrix}
        z_{11} &amp; z_{12} &amp; \cdots &amp; z_{1n} \\
        z_{21} &amp; z_{22} &amp; \cdots &amp; z_{2n} \\
        \vdots &amp; \vdots &amp; \ddots &amp; \vdots \\
        z_{k1} &amp; z_{k2} &amp; \cdots &amp; z_{kn} \\
    \end{bmatrix}
\end{align*}
$$
</p>

<br>
<ul>
<li>批量归一化：对列做归一化</li>
<li>层归一化：对行做归一化，用于小批量样本数较小的时候</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="1257" class="slide " data-line="1257" data-h="15" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>超参数选择</h5></div></div>
<p>超参数对神经网络的性能影响很大，常见的超参数有</p>
<ul>
<li>网络结构：神经元之间的连接关系、层数、每层的神经元数量、激活函数类型</li>
<li>优化参数：优化方法、步长、小批量样本数</li>
<li>正则化系数</li>
</ul>
<br>
<p>超参数优化很难</p>
<ul>
<li>组合优化问题，无法像一般参数那样通过梯度下降方法来优化，也没有一种通用有效的优化方法</li>
<li>评估一组超参数配置的时间代价非常高，从而导致一些黑盒优化方法，如演化算法难以应用</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1276" class="slide " data-line="1276" data-h="15" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>网格搜索</h5></div></div>
<p>尝试所有的超参数组合来寻找一组合适的超参数配置</p>
<br>
<p>设共有<span class="mathjax-exps">$K$</span>个超参数，第<span class="mathjax-exps">$k$</span>个超参数可以取<span class="mathjax-exps">$n_k$</span>个值，那么组合总数为</p>
<p>

$$
\begin{align*}
    n_1 \times n_2 \times \cdots \times n_K
\end{align*}
$$
</p>

<br>
<p>如果超参数是连续的，可以将超参数离散化，选择几个“经验”值，比如正则化系数<span class="mathjax-exps">$\lambda$</span>，可以设置</p>
<p>

$$
\begin{align*}
    \lambda \in \{ 0.01, 0.1, 1, 10, 100 \}
\end{align*}
$$
</p>

<br>
<p>对于连续的超参数，不能简单地按等间隔的方式离散化，需要根据超参数自身的特点进行离散化</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1316" class="slide " data-line="1316" data-h="15" data-v="2">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>随机搜索</h5></div></div>
<p>不同超参数对模型性能的影响有很大差异</p>
<br>
<p>有些超参数对模型性能的影响有限，例如正则化系数；而有些超参数对模型性能影响比较大，例如步长</p>
<br>
<p><span class="blue">采用网格搜索会在不重要的超参数上进行不必要的尝试</span></p>
<br>
<p>随机搜索：对超参数进行随机组合，然后选取一个性能最好的配置</p>
<br>
<p>优点：在实践中更容易实现，一般会比网格搜索更加有效</p>
<br>
<p>缺点：与网格搜索一样，没有利用不同超参数组合之间的相关性，即如果超参数组合比较类似，模型性能也会比较接近，因此这两种搜索方式一般都比较低效</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1344" class="slide " data-line="1344" data-h="15" data-v="3">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>贝叶斯优化</h5></div></div>
<p>根据已试验的超参数组合，猜测可能带来更大收益的组合</p>
<ul>
<li>如何根据已有超参数组合对应的模型性能，得到未知组合的模型性能</li>
<li>如何确定收益</li>
</ul>
<br>
<p>第一个问题通常采用<span class="blue">高斯过程回归</span>，此时<span class="mathjax-exps">$p(\mathtip{g}{泛化风险}|\mathtip{h}{超参数})$</span>为一个正态分布</p>
<p>

$$
\begin{align*}
    \Scal = \{ g_i, h_i \}_{i \in [m]} ~ \Longrightarrow ~ p(\hat{g} | \hat{h}, \Scal)
\end{align*}
$$
</p>

<br>
<p>第二个问题需引入一个收益函数，常见的是期望改善</p>
<p>

$$
\begin{align*}
    \int \max \{ g^\star - g, 0 \} p(g | h, \Scal) \diff g
\end{align*}
$$
</p>

<p>其中<span class="mathjax-exps">$g^\star = \min \{ g_i, i \in [m] \}$</span>是当前已有超参数组合中的最优泛化风险</p>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1385" class="slide " data-line="1385" data-h="15" data-v="4">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>动态资源分配</h5></div></div>
<p>在超参数优化中，如果可以在早期就估计出一个超参数组合的效果会比较差，那么可以提早停止对它的评估，将更多的计算资源留给其它更有潜力的超参数组合</p>
<br>
<p>逐次减半法：</p>
<ul>
<li>将所有计算资源平均分给所有的超参数组合</li>
<li>同时训练每个超参数组合对应的网络一段时间</li>
<li>保留前一半好的组合，转第 1 步</li>
</ul>
<br>
<p>利用 - 探索两难问题：</p>
<ul>
<li>如果超参数组合数越多，得到最佳组合的可能性也越大，但每个组合分到的计算资源就越少，早期的评估结果可能不准</li>
<li>如果超参数组合数越少，每个超参数组合的评估会越准确，但有可能无法得到最优组合</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1408" class="slide " data-line="1408" data-h="15" data-v="5">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>神经架构搜索</h5></div></div>
<p>深度学习：“特征工程”问题 → “网络架构工程”问题</p>
<br>
<p>神经架构搜索 (<strong>n</strong>eural <strong>a</strong>rchitecture <strong>s</strong>earch, NAS)：用神经网络来自动实现网络架构的设计，目前最热 (内) 门 (卷) 的研究方向</p>
<ul>
<li>神经网络的架构可以用一个变长的字符串来描述</li>
<li>用另一个循环神经网络来不断生成新的架构描述</li>
<li>循环神经网络的训练采用强化学习来完成，奖励信号可以为生成的网络在验证集上的性能</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="1424" class="slide " data-line="1424" data-h="16" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>网络正则化</h5></div></div>
<p>权重衰减：每次更新时引入一个衰减系数</p>
<p>

$$
\begin{align*}
    \Wv_{t+1} &amp; \leftarrow (1 - \beta) \Wv_t - \eta \gv_t = \Wv_t - \eta \left( \gv_t + \frac{\beta}{\eta} \Wv_t \right)
\end{align*}
$$
</p>

<ul>
<li>在标准的随机梯度下降中，权重衰减等价于<span class="mathjax-exps">$\ell_2$</span>正则</li>
<li>在较为复杂的优化方法，例如 Adam 中，两者并不等价</li>
</ul>
<br>
<p>提前停止：</p>
<ul>
<li>引入一个和训练集独立的样本集合，称为验证集 (validation set)，验证集上的错误可视为期望错误</li>
<li>当验证集上的错误率不再下降，就停止训练</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1452" class="slide " data-line="1452" data-h="16" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>随机丢弃</h5></div></div>
<p>对每一个神经元都以固定的概率<span class="mathjax-exps">$p$</span>判定是否要保留</p>
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<g id="node2" class="node">
<title>12</title>
<ellipse fill="none" stroke="#586e75" cx="34" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 12&#45;&gt;21 -->
<g id="edge6" class="edge">
<title>12-&gt;21</title>
<path fill="none" stroke="#586e75" d="M49.7854,-219.7146C54.9013,-216.8151 60.6396,-213.6771 66,-211 94.664,-196.6842 103.336,-196.3158 132,-182 135.8528,-180.0758 139.9008,-177.9135 143.7726,-175.7729"></path>
<polygon fill="#586e75" stroke="#586e75" points="148.2146,-173.2854 144.9514,-177.6916 146.0333,-174.5069 143.8521,-175.7284 143.8521,-175.7284 143.8521,-175.7284 146.0333,-174.5069 142.7527,-173.7653 148.2146,-173.2854 148.2146,-173.2854"></polygon>
</g>
<!-- 12&#45;&gt;22 -->
<g id="edge7" class="edge">
<title>12-&gt;22</title>
<path fill="none" stroke="#586e75" d="M49.1768,-219.2558C54.3552,-216.2624 60.2842,-213.185 66,-211 121.7008,-189.7075 141.2992,-203.2925 197,-182 201.1082,-180.4296 205.3266,-178.398 209.2972,-176.2681"></path>
<polygon fill="#586e75" stroke="#586e75" points="213.8232,-173.7442 210.5521,-178.1445 211.6397,-174.9618 209.4563,-176.1794 209.4563,-176.1794 209.4563,-176.1794 211.6397,-174.9618 208.3604,-174.2143 213.8232,-173.7442 213.8232,-173.7442"></polygon>
</g>
<!-- 12&#45;&gt;23 -->
<g id="edge8" class="edge">
<title>12-&gt;23</title>
<path fill="none" stroke="#586e75" d="M49.0902,-219.0166C54.2628,-216.0073 60.2103,-212.9808 66,-211 149.3182,-182.4948 178.6818,-210.5052 262,-182 266.1614,-180.5763 270.4042,-178.6124 274.3823,-176.503"></path>
<polygon fill="#586e75" stroke="#586e75" points="278.9098,-173.9834 275.6349,-178.3809 276.7253,-175.1991 274.5408,-176.4148 274.5408,-176.4148 274.5408,-176.4148 276.7253,-175.1991 273.4466,-174.4487 278.9098,-173.9834 278.9098,-173.9834"></polygon>
</g>
<!-- 12&#45;&gt;24 -->
<g id="edge9" class="edge">
<title>12-&gt;24</title>
<path fill="none" stroke="#586e75" d="M34,-210.8939C34,-203.5688 34,-195.0213 34,-187.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="34,-182.009 36.2501,-187.009 34,-184.509 34.0001,-187.009 34.0001,-187.009 34.0001,-187.009 34,-184.509 31.7501,-187.0091 34,-182.009 34,-182.009"></polygon>
</g>
<!-- 12&#45;&gt;25 -->
<g id="edge10" class="edge">
<title>12-&gt;25</title>
<path fill="none" stroke="#586e75" d="M46.8419,-216.1581C57.08,-205.92 71.4869,-191.5131 82.566,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="86.1848,-176.8152 84.2402,-181.9418 84.417,-178.583 82.6492,-180.3508 82.6492,-180.3508 82.6492,-180.3508 84.417,-178.583 81.0582,-178.7598 86.1848,-176.8152 86.1848,-176.8152"></polygon>
</g>
<!-- 13 -->
<g id="node3" class="node">
<title>13</title>
<ellipse fill="none" stroke="#586e75" cx="294" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 13&#45;&gt;21 -->
<g id="edge11" class="edge">
<title>13-&gt;21</title>
<path fill="none" stroke="#586e75" d="M278.2055,-219.7328C273.0891,-216.8343 267.3528,-213.6923 262,-211 233.7396,-196.7859 225.3525,-196.0295 197,-182 192.8764,-179.9595 188.5212,-177.6778 184.3749,-175.4424"></path>
<polygon fill="#586e75" stroke="#586e75" points="179.9783,-173.0486 185.4456,-173.4635 182.174,-174.2441 184.3696,-175.4396 184.3696,-175.4396 184.3696,-175.4396 182.174,-174.2441 183.2937,-177.4157 179.9783,-173.0486 179.9783,-173.0486"></polygon>
</g>
<!-- 13&#45;&gt;22 -->
<g id="edge12" class="edge">
<title>13-&gt;22</title>
<path fill="none" stroke="#586e75" d="M281.1581,-216.1581C270.92,-205.92 256.5131,-191.5131 245.434,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="241.8152,-176.8152 246.9418,-178.7598 243.583,-178.583 245.3508,-180.3508 245.3508,-180.3508 245.3508,-180.3508 243.583,-178.583 243.7598,-181.9418 241.8152,-176.8152 241.8152,-176.8152"></polygon>
</g>
<!-- 13&#45;&gt;23 -->
<g id="edge13" class="edge">
<title>13-&gt;23</title>
<path fill="none" stroke="#586e75" d="M294,-210.8939C294,-203.5688 294,-195.0213 294,-187.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="294,-182.009 296.2501,-187.009 294,-184.509 294.0001,-187.009 294.0001,-187.009 294.0001,-187.009 294,-184.509 291.7501,-187.0091 294,-182.009 294,-182.009"></polygon>
</g>
<!-- 13&#45;&gt;24 -->
<g id="edge14" class="edge">
<title>13-&gt;24</title>
<path fill="none" stroke="#586e75" d="M278.9098,-219.0166C273.7372,-216.0073 267.7897,-212.9808 262,-211 178.6818,-182.4948 149.3182,-210.5052 66,-182 61.8386,-180.5763 57.5958,-178.6124 53.6177,-176.503"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.0902,-173.9834 54.5534,-174.4487 51.2747,-175.1991 53.4592,-176.4148 53.4592,-176.4148 53.4592,-176.4148 51.2747,-175.1991 52.3651,-178.3809 49.0902,-173.9834 49.0902,-173.9834"></polygon>
</g>
<!-- 13&#45;&gt;25 -->
<g id="edge15" class="edge">
<title>13-&gt;25</title>
<path fill="none" stroke="#586e75" d="M278.8211,-219.2612C273.6425,-216.2682 267.714,-213.1896 262,-211 206.7216,-189.8176 187.4153,-202.8215 132,-182 127.6034,-180.348 123.0707,-178.2023 118.826,-175.971"></path>
<polygon fill="#586e75" stroke="#586e75" points="114.353,-173.5355 119.8203,-173.9505 116.5487,-174.731 118.7443,-175.9266 118.7443,-175.9266 118.7443,-175.9266 116.5487,-174.731 117.6683,-177.9026 114.353,-173.5355 114.353,-173.5355"></polygon>
</g>
<!-- 14 -->
<g id="node4" class="node">
<title>14</title>
<ellipse fill="none" stroke="#586e75" cx="229" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 14&#45;&gt;21 -->
<g id="edge16" class="edge">
<title>14-&gt;21</title>
<path fill="none" stroke="#586e75" d="M216.1581,-216.1581C205.92,-205.92 191.5131,-191.5131 180.434,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="176.8152,-176.8152 181.9418,-178.7598 178.583,-178.583 180.3508,-180.3508 180.3508,-180.3508 180.3508,-180.3508 178.583,-178.583 178.7598,-181.9418 176.8152,-176.8152 176.8152,-176.8152"></polygon>
</g>
<!-- 14&#45;&gt;22 -->
<g id="edge17" class="edge">
<title>14-&gt;22</title>
<path fill="none" stroke="#586e75" d="M229,-210.8939C229,-203.5688 229,-195.0213 229,-187.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="229,-182.009 231.2501,-187.009 229,-184.509 229.0001,-187.009 229.0001,-187.009 229.0001,-187.009 229,-184.509 226.7501,-187.0091 229,-182.009 229,-182.009"></polygon>
</g>
<!-- 14&#45;&gt;23 -->
<g id="edge18" class="edge">
<title>14-&gt;23</title>
<path fill="none" stroke="#586e75" d="M241.8419,-216.1581C252.08,-205.92 266.4869,-191.5131 277.566,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="281.1848,-176.8152 279.2402,-181.9418 279.417,-178.583 277.6492,-180.3508 277.6492,-180.3508 277.6492,-180.3508 279.417,-178.583 276.0582,-178.7598 281.1848,-176.8152 281.1848,-176.8152"></polygon>
</g>
<!-- 14&#45;&gt;24 -->
<g id="edge19" class="edge">
<title>14-&gt;24</title>
<path fill="none" stroke="#586e75" d="M213.8232,-219.2558C208.6448,-216.2624 202.7158,-213.185 197,-211 141.2992,-189.7075 121.7008,-203.2925 66,-182 61.8918,-180.4296 57.6734,-178.398 53.7028,-176.2681"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.1768,-173.7442 54.6396,-174.2143 51.3603,-174.9618 53.5437,-176.1794 53.5437,-176.1794 53.5437,-176.1794 51.3603,-174.9618 52.4479,-178.1445 49.1768,-173.7442 49.1768,-173.7442"></polygon>
</g>
<!-- 14&#45;&gt;25 -->
<g id="edge20" class="edge">
<title>14-&gt;25</title>
<path fill="none" stroke="#586e75" d="M213.2055,-219.7328C208.0891,-216.8343 202.3528,-213.6923 197,-211 168.7396,-196.7859 160.3525,-196.0295 132,-182 127.8764,-179.9595 123.5212,-177.6778 119.3749,-175.4424"></path>
<polygon fill="#586e75" stroke="#586e75" points="114.9783,-173.0486 120.4456,-173.4635 117.174,-174.2441 119.3696,-175.4396 119.3696,-175.4396 119.3696,-175.4396 117.174,-174.2441 118.2937,-177.4157 114.9783,-173.0486 114.9783,-173.0486"></polygon>
</g>
<!-- 15 -->
<g id="node5" class="node">
<title>15</title>
<ellipse fill="none" stroke="#586e75" cx="164" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 15&#45;&gt;21 -->
<g id="edge21" class="edge">
<title>15-&gt;21</title>
<path fill="none" stroke="#586e75" d="M164,-210.8939C164,-203.5688 164,-195.0213 164,-187.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="164,-182.009 166.2501,-187.009 164,-184.509 164.0001,-187.009 164.0001,-187.009 164.0001,-187.009 164,-184.509 161.7501,-187.0091 164,-182.009 164,-182.009"></polygon>
</g>
<!-- 15&#45;&gt;22 -->
<g id="edge22" class="edge">
<title>15-&gt;22</title>
<path fill="none" stroke="#586e75" d="M176.8419,-216.1581C187.08,-205.92 201.4869,-191.5131 212.566,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="216.1848,-176.8152 214.2402,-181.9418 214.417,-178.583 212.6492,-180.3508 212.6492,-180.3508 212.6492,-180.3508 214.417,-178.583 211.0582,-178.7598 216.1848,-176.8152 216.1848,-176.8152"></polygon>
</g>
<!-- 15&#45;&gt;23 -->
<g id="edge23" class="edge">
<title>15-&gt;23</title>
<path fill="none" stroke="#586e75" d="M179.9783,-219.9514C185.3404,-217.0044 191.3849,-213.7785 197,-211 225.3525,-196.9705 233.7396,-196.2141 262,-182 265.8473,-180.0649 269.8927,-177.8975 273.7637,-175.7552"></path>
<polygon fill="#586e75" stroke="#586e75" points="278.2055,-173.2672 274.9428,-177.6737 276.0244,-174.489 273.8432,-175.7107 273.8432,-175.7107 273.8432,-175.7107 276.0244,-174.489 272.7436,-173.7477 278.2055,-173.2672 278.2055,-173.2672"></polygon>
</g>
<!-- 15&#45;&gt;24 -->
<g id="edge24" class="edge">
<title>15-&gt;24</title>
<path fill="none" stroke="#586e75" d="M148.2146,-219.7146C143.0987,-216.8151 137.3604,-213.6771 132,-211 103.336,-196.6842 94.664,-196.3158 66,-182 62.1472,-180.0758 58.0992,-177.9135 54.2274,-175.7729"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.7854,-173.2854 55.2473,-173.7653 51.9667,-174.5069 54.1479,-175.7284 54.1479,-175.7284 54.1479,-175.7284 51.9667,-174.5069 53.0486,-177.6916 49.7854,-173.2854 49.7854,-173.2854"></polygon>
</g>
<!-- 15&#45;&gt;25 -->
<g id="edge25" class="edge">
<title>15-&gt;25</title>
<path fill="none" stroke="#586e75" d="M151.1581,-216.1581C140.92,-205.92 126.5131,-191.5131 115.434,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="111.8152,-176.8152 116.9418,-178.7598 113.583,-178.583 115.3508,-180.3508 115.3508,-180.3508 115.3508,-180.3508 113.583,-178.583 113.7598,-181.9418 111.8152,-176.8152 111.8152,-176.8152"></polygon>
</g>
<!-- 31 -->
<g id="node11" class="node">
<title>31</title>
<ellipse fill="none" stroke="#586e75" cx="164" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;31 -->
<g id="edge26" class="edge">
<title>21-&gt;31</title>
<path fill="none" stroke="#586e75" d="M164,-145.8939C164,-138.5688 164,-130.0213 164,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="164,-117.009 166.2501,-122.009 164,-119.509 164.0001,-122.009 164.0001,-122.009 164.0001,-122.009 164,-119.509 161.7501,-122.0091 164,-117.009 164,-117.009"></polygon>
</g>
<!-- 32 -->
<g id="node12" class="node">
<title>32</title>
<ellipse fill="none" stroke="#586e75" cx="229" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;32 -->
<g id="edge27" class="edge">
<title>21-&gt;32</title>
<path fill="none" stroke="#586e75" d="M176.8419,-151.1581C187.08,-140.92 201.4869,-126.5131 212.566,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="216.1848,-111.8152 214.2402,-116.9418 214.417,-113.583 212.6492,-115.3508 212.6492,-115.3508 212.6492,-115.3508 214.417,-113.583 211.0582,-113.7598 216.1848,-111.8152 216.1848,-111.8152"></polygon>
</g>
<!-- 33 -->
<g id="node13" class="node">
<title>33</title>
<ellipse fill="none" stroke="#586e75" cx="294" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;33 -->
<g id="edge28" class="edge">
<title>21-&gt;33</title>
<path fill="none" stroke="#586e75" d="M179.9783,-154.9514C185.3404,-152.0044 191.3849,-148.7785 197,-146 225.3525,-131.9705 233.7396,-131.2141 262,-117 265.8473,-115.0649 269.8927,-112.8975 273.7637,-110.7552"></path>
<polygon fill="#586e75" stroke="#586e75" points="278.2055,-108.2672 274.9428,-112.6737 276.0244,-109.489 273.8432,-110.7107 273.8432,-110.7107 273.8432,-110.7107 276.0244,-109.489 272.7436,-108.7477 278.2055,-108.2672 278.2055,-108.2672"></polygon>
</g>
<!-- 34 -->
<g id="node14" class="node">
<title>34</title>
<ellipse fill="none" stroke="#586e75" cx="34" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;34 -->
<g id="edge29" class="edge">
<title>21-&gt;34</title>
<path fill="none" stroke="#586e75" d="M148.2146,-154.7146C143.0987,-151.8151 137.3604,-148.6771 132,-146 103.336,-131.6842 94.664,-131.3158 66,-117 62.1472,-115.0758 58.0992,-112.9135 54.2274,-110.7729"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.7854,-108.2854 55.2473,-108.7653 51.9667,-109.5069 54.1479,-110.7284 54.1479,-110.7284 54.1479,-110.7284 51.9667,-109.5069 53.0486,-112.6916 49.7854,-108.2854 49.7854,-108.2854"></polygon>
</g>
<!-- 35 -->
<g id="node15" class="node">
<title>35</title>
<ellipse fill="none" stroke="#586e75" cx="99" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 21&#45;&gt;35 -->
<g id="edge30" class="edge">
<title>21-&gt;35</title>
<path fill="none" stroke="#586e75" d="M151.1581,-151.1581C140.92,-140.92 126.5131,-126.5131 115.434,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="111.8152,-111.8152 116.9418,-113.7598 113.583,-113.583 115.3508,-115.3508 115.3508,-115.3508 115.3508,-115.3508 113.583,-113.583 113.7598,-116.9418 111.8152,-111.8152 111.8152,-111.8152"></polygon>
</g>
<!-- 22&#45;&gt;31 -->
<g id="edge31" class="edge">
<title>22-&gt;31</title>
<path fill="none" stroke="#586e75" d="M216.1581,-151.1581C205.92,-140.92 191.5131,-126.5131 180.434,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="176.8152,-111.8152 181.9418,-113.7598 178.583,-113.583 180.3508,-115.3508 180.3508,-115.3508 180.3508,-115.3508 178.583,-113.583 178.7598,-116.9418 176.8152,-111.8152 176.8152,-111.8152"></polygon>
</g>
<!-- 22&#45;&gt;32 -->
<g id="edge32" class="edge">
<title>22-&gt;32</title>
<path fill="none" stroke="#586e75" d="M229,-145.8939C229,-138.5688 229,-130.0213 229,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="229,-117.009 231.2501,-122.009 229,-119.509 229.0001,-122.009 229.0001,-122.009 229.0001,-122.009 229,-119.509 226.7501,-122.0091 229,-117.009 229,-117.009"></polygon>
</g>
<!-- 22&#45;&gt;33 -->
<g id="edge33" class="edge">
<title>22-&gt;33</title>
<path fill="none" stroke="#586e75" d="M241.8419,-151.1581C252.08,-140.92 266.4869,-126.5131 277.566,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="281.1848,-111.8152 279.2402,-116.9418 279.417,-113.583 277.6492,-115.3508 277.6492,-115.3508 277.6492,-115.3508 279.417,-113.583 276.0582,-113.7598 281.1848,-111.8152 281.1848,-111.8152"></polygon>
</g>
<!-- 22&#45;&gt;34 -->
<g id="edge34" class="edge">
<title>22-&gt;34</title>
<path fill="none" stroke="#586e75" d="M213.8232,-154.2558C208.6448,-151.2624 202.7158,-148.185 197,-146 141.2992,-124.7075 121.7008,-138.2925 66,-117 61.8918,-115.4296 57.6734,-113.398 53.7028,-111.2681"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.1768,-108.7442 54.6396,-109.2143 51.3603,-109.9618 53.5437,-111.1794 53.5437,-111.1794 53.5437,-111.1794 51.3603,-109.9618 52.4479,-113.1445 49.1768,-108.7442 49.1768,-108.7442"></polygon>
</g>
<!-- 22&#45;&gt;35 -->
<g id="edge35" class="edge">
<title>22-&gt;35</title>
<path fill="none" stroke="#586e75" d="M213.2055,-154.7328C208.0891,-151.8343 202.3528,-148.6923 197,-146 168.7396,-131.7859 160.3525,-131.0295 132,-117 127.8764,-114.9595 123.5212,-112.6778 119.3749,-110.4424"></path>
<polygon fill="#586e75" stroke="#586e75" points="114.9783,-108.0486 120.4456,-108.4635 117.174,-109.2441 119.3696,-110.4396 119.3696,-110.4396 119.3696,-110.4396 117.174,-109.2441 118.2937,-112.4157 114.9783,-108.0486 114.9783,-108.0486"></polygon>
</g>
<!-- 23&#45;&gt;31 -->
<g id="edge36" class="edge">
<title>23-&gt;31</title>
<path fill="none" stroke="#586e75" d="M278.2055,-154.7328C273.0891,-151.8343 267.3528,-148.6923 262,-146 233.7396,-131.7859 225.3525,-131.0295 197,-117 192.8764,-114.9595 188.5212,-112.6778 184.3749,-110.4424"></path>
<polygon fill="#586e75" stroke="#586e75" points="179.9783,-108.0486 185.4456,-108.4635 182.174,-109.2441 184.3696,-110.4396 184.3696,-110.4396 184.3696,-110.4396 182.174,-109.2441 183.2937,-112.4157 179.9783,-108.0486 179.9783,-108.0486"></polygon>
</g>
<!-- 23&#45;&gt;32 -->
<g id="edge37" class="edge">
<title>23-&gt;32</title>
<path fill="none" stroke="#586e75" d="M281.1581,-151.1581C270.92,-140.92 256.5131,-126.5131 245.434,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="241.8152,-111.8152 246.9418,-113.7598 243.583,-113.583 245.3508,-115.3508 245.3508,-115.3508 245.3508,-115.3508 243.583,-113.583 243.7598,-116.9418 241.8152,-111.8152 241.8152,-111.8152"></polygon>
</g>
<!-- 23&#45;&gt;33 -->
<g id="edge38" class="edge">
<title>23-&gt;33</title>
<path fill="none" stroke="#586e75" d="M294,-145.8939C294,-138.5688 294,-130.0213 294,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="294,-117.009 296.2501,-122.009 294,-119.509 294.0001,-122.009 294.0001,-122.009 294.0001,-122.009 294,-119.509 291.7501,-122.0091 294,-117.009 294,-117.009"></polygon>
</g>
<!-- 23&#45;&gt;34 -->
<g id="edge39" class="edge">
<title>23-&gt;34</title>
<path fill="none" stroke="#586e75" d="M278.9098,-154.0166C273.7372,-151.0073 267.7897,-147.9808 262,-146 178.6818,-117.4948 149.3182,-145.5052 66,-117 61.8386,-115.5763 57.5958,-113.6124 53.6177,-111.503"></path>
<polygon fill="#586e75" stroke="#586e75" points="49.0902,-108.9834 54.5534,-109.4487 51.2747,-110.1991 53.4592,-111.4148 53.4592,-111.4148 53.4592,-111.4148 51.2747,-110.1991 52.3651,-113.3809 49.0902,-108.9834 49.0902,-108.9834"></polygon>
</g>
<!-- 23&#45;&gt;35 -->
<g id="edge40" class="edge">
<title>23-&gt;35</title>
<path fill="none" stroke="#586e75" d="M278.8211,-154.2612C273.6425,-151.2682 267.714,-148.1896 262,-146 206.7216,-124.8176 187.4153,-137.8215 132,-117 127.6034,-115.348 123.0707,-113.2023 118.826,-110.971"></path>
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</g>
<!-- 24&#45;&gt;31 -->
<g id="edge41" class="edge">
<title>24-&gt;31</title>
<path fill="none" stroke="#586e75" d="M49.7854,-154.7146C54.9013,-151.8151 60.6396,-148.6771 66,-146 94.664,-131.6842 103.336,-131.3158 132,-117 135.8528,-115.0758 139.9008,-112.9135 143.7726,-110.7729"></path>
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</g>
<!-- 24&#45;&gt;32 -->
<g id="edge42" class="edge">
<title>24-&gt;32</title>
<path fill="none" stroke="#586e75" d="M49.1768,-154.2558C54.3552,-151.2624 60.2842,-148.185 66,-146 121.7008,-124.7075 141.2992,-138.2925 197,-117 201.1082,-115.4296 205.3266,-113.398 209.2972,-111.2681"></path>
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</g>
<!-- 24&#45;&gt;33 -->
<g id="edge43" class="edge">
<title>24-&gt;33</title>
<path fill="none" stroke="#586e75" d="M49.0902,-154.0166C54.2628,-151.0073 60.2103,-147.9808 66,-146 149.3182,-117.4948 178.6818,-145.5052 262,-117 266.1614,-115.5763 270.4042,-113.6124 274.3823,-111.503"></path>
<polygon fill="#586e75" stroke="#586e75" points="278.9098,-108.9834 275.6349,-113.3809 276.7253,-110.1991 274.5408,-111.4148 274.5408,-111.4148 274.5408,-111.4148 276.7253,-110.1991 273.4466,-109.4487 278.9098,-108.9834 278.9098,-108.9834"></polygon>
</g>
<!-- 24&#45;&gt;34 -->
<g id="edge44" class="edge">
<title>24-&gt;34</title>
<path fill="none" stroke="#586e75" d="M34,-145.8939C34,-138.5688 34,-130.0213 34,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="34,-117.009 36.2501,-122.009 34,-119.509 34.0001,-122.009 34.0001,-122.009 34.0001,-122.009 34,-119.509 31.7501,-122.0091 34,-117.009 34,-117.009"></polygon>
</g>
<!-- 24&#45;&gt;35 -->
<g id="edge45" class="edge">
<title>24-&gt;35</title>
<path fill="none" stroke="#586e75" d="M46.8419,-151.1581C57.08,-140.92 71.4869,-126.5131 82.566,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="86.1848,-111.8152 84.2402,-116.9418 84.417,-113.583 82.6492,-115.3508 82.6492,-115.3508 82.6492,-115.3508 84.417,-113.583 81.0582,-113.7598 86.1848,-111.8152 86.1848,-111.8152"></polygon>
</g>
<!-- 25&#45;&gt;31 -->
<g id="edge46" class="edge">
<title>25-&gt;31</title>
<path fill="none" stroke="#586e75" d="M111.8419,-151.1581C122.08,-140.92 136.4869,-126.5131 147.566,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="151.1848,-111.8152 149.2402,-116.9418 149.417,-113.583 147.6492,-115.3508 147.6492,-115.3508 147.6492,-115.3508 149.417,-113.583 146.0582,-113.7598 151.1848,-111.8152 151.1848,-111.8152"></polygon>
</g>
<!-- 25&#45;&gt;32 -->
<g id="edge47" class="edge">
<title>25-&gt;32</title>
<path fill="none" stroke="#586e75" d="M114.9783,-154.9514C120.3404,-152.0044 126.3849,-148.7785 132,-146 160.3525,-131.9705 168.7396,-131.2141 197,-117 200.8473,-115.0649 204.8927,-112.8975 208.7637,-110.7552"></path>
<polygon fill="#586e75" stroke="#586e75" points="213.2055,-108.2672 209.9428,-112.6737 211.0244,-109.489 208.8432,-110.7107 208.8432,-110.7107 208.8432,-110.7107 211.0244,-109.489 207.7436,-108.7477 213.2055,-108.2672 213.2055,-108.2672"></polygon>
</g>
<!-- 25&#45;&gt;33 -->
<g id="edge48" class="edge">
<title>25-&gt;33</title>
<path fill="none" stroke="#586e75" d="M114.353,-154.4645C119.774,-151.4145 126.0132,-148.2495 132,-146 187.4153,-125.1785 206.7216,-138.1824 262,-117 266.107,-115.4262 270.3247,-113.3932 274.2952,-111.2627"></path>
<polygon fill="#586e75" stroke="#586e75" points="278.8211,-108.7388 275.5502,-113.1392 276.6377,-109.9564 274.4543,-111.1741 274.4543,-111.1741 274.4543,-111.1741 276.6377,-109.9564 273.3584,-109.209 278.8211,-108.7388 278.8211,-108.7388"></polygon>
</g>
<!-- 25&#45;&gt;34 -->
<g id="edge49" class="edge">
<title>25-&gt;34</title>
<path fill="none" stroke="#586e75" d="M86.1581,-151.1581C75.92,-140.92 61.5131,-126.5131 50.434,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="46.8152,-111.8152 51.9418,-113.7598 48.583,-113.583 50.3508,-115.3508 50.3508,-115.3508 50.3508,-115.3508 48.583,-113.583 48.7598,-116.9418 46.8152,-111.8152 46.8152,-111.8152"></polygon>
</g>
<!-- 25&#45;&gt;35 -->
<g id="edge50" class="edge">
<title>25-&gt;35</title>
<path fill="none" stroke="#586e75" d="M99,-145.8939C99,-138.5688 99,-130.0213 99,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="99,-117.009 101.2501,-122.009 99,-119.509 99.0001,-122.009 99.0001,-122.009 99.0001,-122.009 99,-119.509 96.7501,-122.0091 99,-117.009 99,-117.009"></polygon>
</g>
<!-- 41 -->
<g id="node16" class="node">
<title>41</title>
<ellipse fill="none" stroke="#586e75" cx="164" cy="-34" rx="18" ry="18"></ellipse>
</g>
<!-- 31&#45;&gt;41 -->
<g id="edge51" class="edge">
<title>31-&gt;41</title>
<path fill="none" stroke="#586e75" d="M164,-80.8939C164,-73.5688 164,-65.0213 164,-57.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="164,-52.009 166.2501,-57.009 164,-54.509 164.0001,-57.009 164.0001,-57.009 164.0001,-57.009 164,-54.509 161.7501,-57.0091 164,-52.009 164,-52.009"></polygon>
</g>
<!-- 32&#45;&gt;41 -->
<g id="edge52" class="edge">
<title>32-&gt;41</title>
<path fill="none" stroke="#586e75" d="M216.1581,-86.1581C205.92,-75.92 191.5131,-61.5131 180.434,-50.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="176.8152,-46.8152 181.9418,-48.7598 178.583,-48.583 180.3508,-50.3508 180.3508,-50.3508 180.3508,-50.3508 178.583,-48.583 178.7598,-51.9418 176.8152,-46.8152 176.8152,-46.8152"></polygon>
</g>
<!-- 33&#45;&gt;41 -->
<g id="edge53" class="edge">
<title>33-&gt;41</title>
<path fill="none" stroke="#586e75" d="M278.1604,-89.821C273.0415,-86.9272 267.3152,-83.7657 262,-81 235.8108,-67.3727 205.0286,-52.847 185.0495,-43.6111"></path>
<polygon fill="#586e75" stroke="#586e75" points="180.4607,-41.495 185.9434,-41.5456 182.7309,-42.5419 185.0012,-43.5889 185.0012,-43.5889 185.0012,-43.5889 182.7309,-42.5419 184.0589,-45.6321 180.4607,-41.495 180.4607,-41.495"></polygon>
</g>
<!-- 34&#45;&gt;41 -->
<g id="edge54" class="edge">
<title>34-&gt;41</title>
<path fill="none" stroke="#586e75" d="M49.8396,-89.821C54.9585,-86.9272 60.6848,-83.7657 66,-81 92.1892,-67.3727 122.9714,-52.847 142.9505,-43.6111"></path>
<polygon fill="#586e75" stroke="#586e75" points="147.5393,-41.495 143.9411,-45.6321 145.2691,-42.5419 142.9988,-43.5889 142.9988,-43.5889 142.9988,-43.5889 145.2691,-42.5419 142.0566,-41.5456 147.5393,-41.495 147.5393,-41.495"></polygon>
</g>
<!-- 35&#45;&gt;41 -->
<g id="edge55" class="edge">
<title>35-&gt;41</title>
<path fill="none" stroke="#586e75" d="M111.8419,-86.1581C122.08,-75.92 136.4869,-61.5131 147.566,-50.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="151.1848,-46.8152 149.2402,-51.9418 149.417,-48.583 147.6492,-50.3508 147.6492,-50.3508 147.6492,-50.3508 149.417,-48.583 146.0582,-48.7598 151.1848,-46.8152 151.1848,-46.8152"></polygon>
</g>
<!-- 51 -->
<g id="node17" class="node">
<title>51</title>
<ellipse fill="none" stroke="#586e75" cx="424" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 61 -->
<g id="node22" class="node">
<title>61</title>
<ellipse fill="none" stroke="#586e75" cx="489" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 51&#45;&gt;61 -->
<g id="edge56" class="edge">
<title>51-&gt;61</title>
<path fill="none" stroke="#586e75" d="M436.8419,-216.1581C447.08,-205.92 461.4869,-191.5131 472.566,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="476.1848,-176.8152 474.2402,-181.9418 474.417,-178.583 472.6492,-180.3508 472.6492,-180.3508 472.6492,-180.3508 474.417,-178.583 471.0582,-178.7598 476.1848,-176.8152 476.1848,-176.8152"></polygon>
</g>
<!-- 62 -->
<g id="node23" class="node">
<title>62</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="554" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 51&#45;&gt;62 -->
<g id="edge71" class="edge">
<title>51-&gt;62</title>
</g>
<!-- 63 -->
<g id="node24" class="node">
<title>63</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="619" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 51&#45;&gt;63 -->
<g id="edge72" class="edge">
<title>51-&gt;63</title>
</g>
<!-- 64 -->
<g id="node25" class="node">
<title>64</title>
<ellipse fill="none" stroke="#586e75" cx="359" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 51&#45;&gt;64 -->
<g id="edge57" class="edge">
<title>51-&gt;64</title>
<path fill="none" stroke="#586e75" d="M411.1581,-216.1581C400.92,-205.92 386.5131,-191.5131 375.434,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="371.8152,-176.8152 376.9418,-178.7598 373.583,-178.583 375.3508,-180.3508 375.3508,-180.3508 375.3508,-180.3508 373.583,-178.583 373.7598,-181.9418 371.8152,-176.8152 371.8152,-176.8152"></polygon>
</g>
<!-- 65 -->
<g id="node26" class="node">
<title>65</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="424" cy="-164" rx="18" ry="18"></ellipse>
</g>
<!-- 51&#45;&gt;65 -->
<g id="edge73" class="edge">
<title>51-&gt;65</title>
</g>
<!-- 52 -->
<g id="node18" class="node">
<title>52</title>
<ellipse fill="none" stroke="#586e75" cx="359" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 52&#45;&gt;61 -->
<g id="edge58" class="edge">
<title>52-&gt;61</title>
<path fill="none" stroke="#586e75" d="M374.9783,-219.9514C380.3404,-217.0044 386.3849,-213.7785 392,-211 420.3525,-196.9705 428.7396,-196.2141 457,-182 460.8473,-180.0649 464.8927,-177.8975 468.7637,-175.7552"></path>
<polygon fill="#586e75" stroke="#586e75" points="473.2055,-173.2672 469.9428,-177.6737 471.0244,-174.489 468.8432,-175.7107 468.8432,-175.7107 468.8432,-175.7107 471.0244,-174.489 467.7436,-173.7477 473.2055,-173.2672 473.2055,-173.2672"></polygon>
</g>
<!-- 52&#45;&gt;62 -->
<g id="edge74" class="edge">
<title>52-&gt;62</title>
</g>
<!-- 52&#45;&gt;63 -->
<g id="edge75" class="edge">
<title>52-&gt;63</title>
</g>
<!-- 52&#45;&gt;64 -->
<g id="edge59" class="edge">
<title>52-&gt;64</title>
<path fill="none" stroke="#586e75" d="M359,-210.8939C359,-203.5688 359,-195.0213 359,-187.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="359,-182.009 361.2501,-187.009 359,-184.509 359.0001,-187.009 359.0001,-187.009 359.0001,-187.009 359,-184.509 356.7501,-187.0091 359,-182.009 359,-182.009"></polygon>
</g>
<!-- 52&#45;&gt;65 -->
<g id="edge76" class="edge">
<title>52-&gt;65</title>
</g>
<!-- 53 -->
<g id="node19" class="node">
<title>53</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="619" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 53&#45;&gt;61 -->
<g id="edge80" class="edge">
<title>53-&gt;61</title>
</g>
<!-- 53&#45;&gt;62 -->
<g id="edge81" class="edge">
<title>53-&gt;62</title>
</g>
<!-- 53&#45;&gt;63 -->
<g id="edge82" class="edge">
<title>53-&gt;63</title>
</g>
<!-- 53&#45;&gt;64 -->
<g id="edge83" class="edge">
<title>53-&gt;64</title>
</g>
<!-- 53&#45;&gt;65 -->
<g id="edge84" class="edge">
<title>53-&gt;65</title>
</g>
<!-- 54 -->
<g id="node20" class="node">
<title>54</title>
<ellipse fill="none" stroke="#586e75" cx="554" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 54&#45;&gt;61 -->
<g id="edge60" class="edge">
<title>54-&gt;61</title>
<path fill="none" stroke="#586e75" d="M541.1581,-216.1581C530.92,-205.92 516.5131,-191.5131 505.434,-180.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="501.8152,-176.8152 506.9418,-178.7598 503.583,-178.583 505.3508,-180.3508 505.3508,-180.3508 505.3508,-180.3508 503.583,-178.583 503.7598,-181.9418 501.8152,-176.8152 501.8152,-176.8152"></polygon>
</g>
<!-- 54&#45;&gt;62 -->
<g id="edge77" class="edge">
<title>54-&gt;62</title>
</g>
<!-- 54&#45;&gt;63 -->
<g id="edge78" class="edge">
<title>54-&gt;63</title>
</g>
<!-- 54&#45;&gt;64 -->
<g id="edge61" class="edge">
<title>54-&gt;64</title>
<path fill="none" stroke="#586e75" d="M538.8211,-219.2612C533.6425,-216.2682 527.714,-213.1896 522,-211 466.7216,-189.8176 447.4153,-202.8215 392,-182 387.6034,-180.348 383.0707,-178.2023 378.826,-175.971"></path>
<polygon fill="#586e75" stroke="#586e75" points="374.353,-173.5355 379.8203,-173.9505 376.5487,-174.731 378.7443,-175.9266 378.7443,-175.9266 378.7443,-175.9266 376.5487,-174.731 377.6683,-177.9026 374.353,-173.5355 374.353,-173.5355"></polygon>
</g>
<!-- 54&#45;&gt;65 -->
<g id="edge79" class="edge">
<title>54-&gt;65</title>
</g>
<!-- 55 -->
<g id="node21" class="node">
<title>55</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="489" cy="-229" rx="18" ry="18"></ellipse>
</g>
<!-- 55&#45;&gt;61 -->
<g id="edge85" class="edge">
<title>55-&gt;61</title>
</g>
<!-- 55&#45;&gt;62 -->
<g id="edge86" class="edge">
<title>55-&gt;62</title>
</g>
<!-- 55&#45;&gt;63 -->
<g id="edge87" class="edge">
<title>55-&gt;63</title>
</g>
<!-- 55&#45;&gt;64 -->
<g id="edge88" class="edge">
<title>55-&gt;64</title>
</g>
<!-- 55&#45;&gt;65 -->
<g id="edge89" class="edge">
<title>55-&gt;65</title>
</g>
<!-- 71 -->
<g id="node27" class="node">
<title>71</title>
<ellipse fill="none" stroke="#586e75" cx="489" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 61&#45;&gt;71 -->
<g id="edge62" class="edge">
<title>61-&gt;71</title>
<path fill="none" stroke="#586e75" d="M489,-145.8939C489,-138.5688 489,-130.0213 489,-122.2449"></path>
<polygon fill="#586e75" stroke="#586e75" points="489,-117.009 491.2501,-122.009 489,-119.509 489.0001,-122.009 489.0001,-122.009 489.0001,-122.009 489,-119.509 486.7501,-122.0091 489,-117.009 489,-117.009"></polygon>
</g>
<!-- 72 -->
<g id="node28" class="node">
<title>72</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="554" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 61&#45;&gt;72 -->
<g id="edge90" class="edge">
<title>61-&gt;72</title>
</g>
<!-- 73 -->
<g id="node29" class="node">
<title>73</title>
<ellipse fill="none" stroke="#586e75" cx="619" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 61&#45;&gt;73 -->
<g id="edge63" class="edge">
<title>61-&gt;73</title>
<path fill="none" stroke="#586e75" d="M504.9783,-154.9514C510.3404,-152.0044 516.3849,-148.7785 522,-146 550.3525,-131.9705 558.7396,-131.2141 587,-117 590.8473,-115.0649 594.8927,-112.8975 598.7637,-110.7552"></path>
<polygon fill="#586e75" stroke="#586e75" points="603.2055,-108.2672 599.9428,-112.6737 601.0244,-109.489 598.8432,-110.7107 598.8432,-110.7107 598.8432,-110.7107 601.0244,-109.489 597.7436,-108.7477 603.2055,-108.2672 603.2055,-108.2672"></polygon>
</g>
<!-- 74 -->
<g id="node30" class="node">
<title>74</title>
<ellipse fill="none" stroke="#586e75" stroke-dasharray="5,2" cx="359" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 61&#45;&gt;74 -->
<g id="edge91" class="edge">
<title>61-&gt;74</title>
</g>
<!-- 75 -->
<g id="node31" class="node">
<title>75</title>
<ellipse fill="none" stroke="#586e75" cx="424" cy="-99" rx="18" ry="18"></ellipse>
</g>
<!-- 61&#45;&gt;75 -->
<g id="edge64" class="edge">
<title>61-&gt;75</title>
<path fill="none" stroke="#586e75" d="M476.1581,-151.1581C465.92,-140.92 451.5131,-126.5131 440.434,-115.434"></path>
<polygon fill="#586e75" stroke="#586e75" points="436.8152,-111.8152 441.9418,-113.7598 438.583,-113.583 440.3508,-115.3508 440.3508,-115.3508 440.3508,-115.3508 438.583,-113.583 438.7598,-116.9418 436.8152,-111.8152 436.8152,-111.8152"></polygon>
</g>
<!-- 62&#45;&gt;71 -->
<g id="edge94" class="edge">
<title>62-&gt;71</title>
</g>
<!-- 62&#45;&gt;72 -->
<g id="edge95" class="edge">
<title>62-&gt;72</title>
</g>
<!-- 62&#45;&gt;73 -->
<g id="edge96" class="edge">
<title>62-&gt;73</title>
</g>
<!-- 62&#45;&gt;74 -->
<g id="edge97" class="edge">
<title>62-&gt;74</title>
</g>
<!-- 62&#45;&gt;75 -->
<g id="edge98" class="edge">
<title>62-&gt;75</title>
</g>
<!-- 63&#45;&gt;71 -->
<g id="edge99" class="edge">
<title>63-&gt;71</title>
</g>
<!-- 63&#45;&gt;72 -->
<g id="edge100" class="edge">
<title>63-&gt;72</title>
</g>
<!-- 63&#45;&gt;73 -->
<g id="edge101" class="edge">
<title>63-&gt;73</title>
</g>
<!-- 63&#45;&gt;74 -->
<g id="edge102" class="edge">
<title>63-&gt;74</title>
</g>
<!-- 63&#45;&gt;75 -->
<g id="edge103" class="edge">
<title>63-&gt;75</title>
</g>
<!-- 64&#45;&gt;71 -->
<g id="edge65" class="edge">
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</p><div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section><section data-notes="" lineno="1462" class="slide " data-line="1462" data-h="17" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>数据增强</h5></div></div>
<p>深层神经网络需要大量的训练数据才能获得比较理想的效果</p>
<br>
<p>数据量有限的情况下，可以通过数据增强来增加数据量，避免过拟合</p>
<br>
<p>目前数据增强主要用于图像数据，文本等其它类型的数据还没有太好的方法</p>
<br>
<p>常见的增强方法：</p>
<ul>
<li>旋转：将图像随机旋转一定角度</li>
<li>翻转：将图像沿水平或垂直方法随机翻转一定角度</li>
<li>缩放：将图像放大或缩小一定比例</li>
<li>平移：将图像沿水平或垂直方法平移一定距离</li>
<li>加噪声：加入随机噪声</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section><section vertical="true" data-notes="" lineno="1488" class="slide " data-line="1488" data-h="17" data-v="1">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>mixup</h5></div></div>
<p>利用任意两个样本<span class="mathjax-exps">$(\xv_a, y_a)$</span>、<span class="mathjax-exps">$(\xv_b, y_b)$</span>生成新样本</p>
<p>

$$
\begin{align*}
    (\beta \xv_a + (1 - \beta) \xv_b, \beta y_a + (1 - \beta) y_b)
\end{align*}
$$
</p>

<br>
<p>假设模型已经有能力预测<span class="mathjax-exps">$y_a = f(\xv_a)$</span>、<span class="mathjax-exps">$y_b = f(\xv_b)$</span>，那么此时还需满足</p>
<p>

$$
\begin{align*}
    f(\beta \xv_a + (1 - \beta) \xv_b) = \beta y_a + (1 - \beta) y_b = \beta f(\xv_a) + (1 - \beta) f(\xv_b)
\end{align*}
$$
</p>

<br>
<p>这个函数方程的解是线性函数，即 mixup 希望学到的<span class="mathjax-exps">$f$</span>是线性函数</p>
<div class="bottom6"></div>
<h3 class="mume-header" id="%E6%8A%AB%E7%9D%80%E6%95%B0%E6%8D%AE%E5%A2%9E%E5%BC%BA%E5%A4%96%E8%A1%A3%E7%9A%84%E6%AD%A3%E5%88%99%E5%8C%96%E6%96%B9%E6%B3%95">披着数据增强外衣的正则化方法！</h3>

<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section></section><section data-notes="" lineno="1528" class="slide " data-line="1528" data-h="18" data-v="0">
<div class="header"><img class="hust" src=""><div class="title"><hr class="hr_top"><h5>总结 当代炼丹术</h5></div></div>
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一个优秀丹师的自我修养：</p>
<ul>
<li>灵材品质差要会手动增强，旋转、翻转、缩放、平移、加噪声、标记平滑</li>
<li>因材制宜设计灵阵，空间属性灵材用卷积类灵阵，时间属性灵材用循环类...</li>
<li>仔细观察丹炉状态，防止爆炉，若最终仙丹成色不好则改进配置重新来过</li>
</ul>
<div class="footer"><hr class="hr_bottom"><div class="multi_column"><h6 class="bottom_left">图神经网络导论</h6><h6 class="bottom_center">神经网络</h6><h6 class="bottom_right">tengzhang@hust.edu.cn</h6></div></div>
</section>
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